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C# How to: Oil Painting and Cartoon Filter

Article Purpose

This article illustrates and provides a discussion and implementation of Image Oil Painting Filters and related Image Cartoon Filters.

Sunflower: Oil Painting, Filter 5, Levels 30, Cartoon Threshold 30

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Using the Sample Application

A sample application accompanies this article. The sample application creates a visual implementation of the concepts discussed throughout this article. Source/input images can be selected from the local file system and if desired filter result images can be saved to the local file system.

The two main types of functionality exposed by the sample application can be described as Image Oil Painting Filters and Image Cartoon Filters. The user interface provides the following user input options:

Filter Size – The number of neighbouring pixels used in calculating each individual pixel value in regards to an Oil Painting Filter. Higher Filter sizes relate to a more intense Oil Painting Filter being applied. Lower Filter sizes relate to less intense Oil Painting Filters being applied.
Intensity Levels – Represents the number of Intensity Levels implemented when applying an Oil Painting Filter. Higher values result in a broader range of colour intensities forming part of the result image. Lower values will reduce the range of colour intensities forming part of the result image.
Cartoon Filter – A Boolean value indicating whether or not in addition to an Oil Painting Filter if a Cartoon Filter should also be applied.
Threshold – Only applicable when applying a Cartoon Filter. This option represents the threshold value implemented in determining whether a pixel forms part of an Image Edge. Lower Values result in more image edges being highlighted. Higher values result in less image edges being highlighted.

The following image is screenshot of the Oil Painting Cartoon Filter sample application in action:

Rose: Oil Painting, Filter 15, Levels 10

Image Oil Painting Filter

The Image Oil Painting Filter consists of two main components: colour gradients and pixel colour intensities. As implied by the title when implementing this image filter resulting images are similar in appearance to images of Oil Paintings. Result images express a lesser degree of image detail when compared to source/input images. This filter also tends to output images which appear to have smaller colour ranges.

Four steps are required when implementing an Oil Painting Filter, indicated as follows:

Iterate each pixel – Every pixel forming part of the source/input image should be iterated. When iterating a pixel determine the neighbouring pixel values based on the specified filter size/filter range.
Calculate Colour Intensity - Determine the Colour Intensity of each pixel being iterated and that of the neighbouring pixels. The neighbouring pixels included should extend to a range determined by the Filter Size specified. The calculated value should be reduced in order to match a value ranging from zero to the number of Intensity Levels specified.
Determine maximum neighbourhood colour intensity – When calculating the colour intensities of a pixel neighbourhood determine the maximum intensity value. In addition, record the occurrence of each intensity level and sum each of the Red, Green and Blue pixel colour component values equating to the same intensity level.
Assign the result pixel – The value assigned to the corresponding pixel in the resulting image equates to the pixel colour sum total, where those pixels expressed the same intensity level. The sum total should be averaged by dividing the colour sum total by the intensity level occurrence.

Roses: Oil Painting, Filter 11, Levels 60, Cartoon Threshold 80

When calculating colour intensity reduced to fit the number of levels specified the algorithm implemented can be expressed as follows:

In the algorithm listed above the variables implemented can be explained as follows:

I – Intensity: The calculated intensity value.
R – Red: The value of a pixel’s Red colour component.
G – Green: The value of a pixel’s Green colour component.
B – Blue: The value of a pixel’s Blue colour component.
l – Number of intensity levels: The maximum number of intensity levels specified.

Rose: Oil Painting, Filter 15, Levels 30

Cartoon Filter implementing Edge Detection

A Cartoon Filter effect can be achieved by combining an Image Oil Painting filter and an Edge Detection Filter. The Oil Painting filter has the effect of creating more gradual image colour gradients, in other words reducing image edge intensity.

The steps required in implementing a Cartoon filter can be listed as follows:

Apply Oil Painting filter – Applying an Oil Painting Filter creates the perception of result images having been painted by hand.
Implement Edge Detection – Using the original source/input image create a new binary image detailing image edges.
Overlay edges on Oil Painting image – Iterate each pixel forming part of the edge detected image. If the pixel being iterated forms part of an edge, the related pixel in the Oil Painting filtered image should be set to black. Because the edge detected image was created as a binary image, a pixel forms part of an edge should that pixel equate to white.

Daisy: Oil Painting, Filter 7, Levels 30, Cartoon Threshold 40

In the sample source code edge detection has been implemented through Gradient Based Edge Detection. This method of edge detection compares the difference in colour gradients between a pixel’s neighbouring pixels. A pixel forms part of an edge if the difference in neighbouring pixel colour values exceeds a specified threshold value. The steps involved in Gradient Based Edge Detection as follows:

Iterate each pixel – Each pixel forming part of a source/input image should be iterated.
Determine Horizontal and Vertical Gradients – Calculate the colour value difference between the currently iterated pixel’s left and right neighbour pixel as well as the top and bottom neighbour pixel. If the gradient exceeds the specified threshold continue to step 8.
Determine Horizontal Gradient – Calculate the colour value difference between the currently iterated pixel’s left and right neighbour pixel. If the gradient exceeds the specified threshold continue to step 8.
Determine Vertical Gradient – Calculate the colour value difference between the currently iterated pixel’s top and bottom neighbour pixel. If the gradient exceeds the specified threshold continue to step 8.
Determine Diagonal Gradients – Calculate the colour value difference between the currently iterated pixel’s North-Western and South-Eastern neighbour pixel as well as the North-Eastern and South-Western neighbour pixel. If the gradient exceeds the specified threshold continue to step 8.
Determine NW-SE Gradient – Calculate the colour value difference between the currently iterated pixel’s North-Western and South-Eastern neighbour pixel. If the gradient exceeds the specified threshold continue to step 8.
Determine NE-SW Gradient – Calculate the colour value difference between the currently iterated pixel’s North-Eastern and South-Western neighbour pixel.
Determine and set result pixel value – If any of the six gradients calculated exceeded the specified threshold value set the related pixel in the resulting image to white, if not, set the related pixel to black.

Rose: Oil Painting, Filter 9, Levels 30

Implementing an Oil Painting Filter

The sample source code defines the OilPaintFilter method, an extension method targeting the Bitmap class. This method determines the maximum colour intensity from a pixel’s neighbouring pixels. The definition detailed as follows:

public static Bitmap OilPaintFilter(this Bitmap sourceBitmap, 
                                       int levels, 
                                       int filterSize) 
{
    BitmapData sourceData = 
               sourceBitmap.LockBits(new Rectangle(0, 0, 
               sourceBitmap.Width, sourceBitmap.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 


    byte[] pixelBuffer = new byte[sourceData.Stride * 
                                  sourceData.Height]; 


    byte[] resultBuffer = new byte[sourceData.Stride * 
                                   sourceData.Height]; 


    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, 
                               pixelBuffer.Length); 


    sourceBitmap.UnlockBits(sourceData); 


    int[] intensityBin = new int [levels]; 
    int[] blueBin = new int [levels]; 
    int[] greenBin = new int [levels]; 
    int[] redBin = new int [levels]; 


    levels = levels - 1; 


    int filterOffset = (filterSize - 1) / 2; 
    int byteOffset = 0; 
    int calcOffset = 0; 
    int currentIntensity = 0; 
    int maxIntensity = 0; 
    int maxIndex = 0; 


    double blue = 0; 
    double green = 0; 
    double red = 0; 


    for (int offsetY = filterOffset; offsetY < 
        sourceBitmap.Height - filterOffset; offsetY++) 
    {
        for (int offsetX = filterOffset; offsetX < 
            sourceBitmap.Width - filterOffset; offsetX++) 
        {
            blue = green = red = 0; 


            currentIntensity = maxIntensity = maxIndex = 0; 


            intensityBin = new int[levels + 1]; 
            blueBin = new int[levels + 1]; 
            greenBin = new int[levels + 1]; 
            redBin = new int[levels + 1]; 


            byteOffset = offsetY * 
            sourceData.Stride + offsetX * 4; 


            for (int filterY = -filterOffset; 
                filterY <= filterOffset; filterY++) 
            {
                for (int filterX = -filterOffset; 
                    filterX <= filterOffset; filterX++) 
                {
                    calcOffset = byteOffset + 
                                 (filterX * 4) + 
                                 (filterY * sourceData.Stride); 


                    currentIntensity = (int )Math.Round(((double) 
                               (pixelBuffer[calcOffset] + 
                               pixelBuffer[calcOffset + 1] + 
                               pixelBuffer[calcOffset + 2]) / 3.0 * 
                               (levels)) / 255.0); 


                    intensityBin[currentIntensity] += 1; 
                    blueBin[currentIntensity] += pixelBuffer[calcOffset]; 
                    greenBin[currentIntensity] += pixelBuffer[calcOffset + 1]; 
                    redBin[currentIntensity] += pixelBuffer[calcOffset + 2]; 


                   if (intensityBin[currentIntensity] > maxIntensity) 
                    {
                        maxIntensity = intensityBin[currentIntensity]; 
                        maxIndex = currentIntensity; 
                    }
                }
            }


            blue = blueBin[maxIndex] / maxIntensity; 
            green = greenBin[maxIndex] / maxIntensity; 
            red = redBin[maxIndex] / maxIntensity; 


            resultBuffer[byteOffset] = ClipByte(blue); 
            resultBuffer[byteOffset + 1] = ClipByte(green); 
            resultBuffer[byteOffset + 2] = ClipByte(red); 
            resultBuffer[byteOffset + 3] = 255; 
 
        }
    }


    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, 
                                     sourceBitmap.Height); 


    BitmapData resultData = 
               resultBitmap.LockBits(new Rectangle(0, 0, 
               resultBitmap.Width, resultBitmap.Height), 
               ImageLockMode.WriteOnly, 
               PixelFormat.Format32bppArgb); 


    Marshal.Copy(resultBuffer, 0, resultData.Scan0, 
                               resultBuffer.Length); 


    resultBitmap.UnlockBits(resultData); 


    return resultBitmap; 
}

Rose: Oil Painting, Filter 7, Levels 20, Cartoon Threshold 20

Implementing a Cartoon Filter using Edge Detection

The sample source code defines the CheckThreshold method. The purpose of this method to determine the difference in colour between two pixels. In addition this method compares the colour difference and the specified threshold value. The following code snippet provides the implementation:

private static bool CheckThreshold(byte[] pixelBuffer,  
                                   int offset1, int offset2,  
                                   ref int gradientValue,  
                                   byte threshold,  
                                   int divideBy = 1) 
{ 
    gradientValue += 
    Math.Abs(pixelBuffer[offset1] - 
    pixelBuffer[offset2]) / divideBy; 


    gradientValue += 
    Math.Abs(pixelBuffer[offset1 + 1] - 
    pixelBuffer[offset2 + 1]) / divideBy; 


    gradientValue += 
    Math.Abs(pixelBuffer[offset1 + 2] - 
    pixelBuffer[offset2 + 2]) / divideBy; 


    return (gradientValue >= threshold); 
}

Rose: Oil Painting, Filter 13, Levels 15

The GradientBasedEdgeDetectionFilter method has been defined as an extension method targeting the Bitmap class. This method iterates each pixel forming part of the source/input image. Whilst iterating pixels the GradientBasedEdgeDetectionFilter extension method determines if the colour gradients in various directions exceeds the specified threshold value. A pixel is considered as part of an edge if a colour gradient exceeds the threshold value. The implementation as follows:

public static Bitmap GradientBasedEdgeDetectionFilter( 
                        this Bitmap sourceBitmap, 
                        byte threshold = 0) 
{ 
    BitmapData sourceData = 
               sourceBitmap.LockBits(new Rectangle (0, 0, 
               sourceBitmap.Width, sourceBitmap.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 


    byte[] pixelBuffer = new byte[sourceData.Stride * sourceData.Height]; 
    byte[] resultBuffer = new byte[sourceData.Stride * sourceData.Height]; 


    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, pixelBuffer.Length); 
    sourceBitmap.UnlockBits(sourceData); 


    int sourceOffset = 0, gradientValue = 0; 
    bool exceedsThreshold = false; 


    for(int offsetY = 1; offsetY < sourceBitmap.Height - 1; offsetY++) 
    { 
        for(int offsetX = 1; offsetX < sourceBitmap.Width - 1; offsetX++) 
        {
            sourceOffset = offsetY * sourceData.Stride + offsetX * 4; 
            gradientValue = 0; 
            exceedsThreshold = true ; 


            // Horizontal Gradient 
            CheckThreshold(pixelBuffer,  
                           sourceOffset - 4,  
                           sourceOffset + 4,  
                           ref gradientValue, threshold, 2); 
            // Vertical Gradient 
            exceedsThreshold =  
            CheckThreshold(pixelBuffer,  
                           sourceOffset - sourceData.Stride,  
                           sourceOffset + sourceData.Stride,  
                           ref gradientValue, threshold, 2); 


            if (exceedsThreshold == false ) 
            {
                gradientValue = 0; 


                // Horizontal Gradient 
                exceedsThreshold =  
                CheckThreshold(pixelBuffer,  
                               sourceOffset - 4,  
                               sourceOffset + 4,  
                               ref gradientValue, threshold); 


                if (exceedsThreshold == false ) 
                { 
                    gradientValue = 0; 
 
                    // Vertical Gradient 
                    exceedsThreshold =  
                    CheckThreshold(pixelBuffer,  
                                   sourceOffset - sourceData.Stride,  
                                   sourceOffset + sourceData.Stride,  
                                   ref gradientValue, threshold); 


                    if (exceedsThreshold == false ) 
                    {
                        gradientValue = 0; 
 
                        // Diagonal Gradient : NW-SE 
                        CheckThreshold(pixelBuffer,  
                                       sourceOffset - 4 - sourceData.Stride,  
                                       sourceOffset + 4 + sourceData.Stride,  
                                       ref gradientValue, threshold, 2); 
                        // Diagonal Gradient : NE-SW 
                        exceedsThreshold =  
                        CheckThreshold(pixelBuffer,  
                                       sourceOffset - sourceData.Stride + 4,  
                                       sourceOffset - 4 + sourceData.Stride,  
                                       ref gradientValue, threshold, 2); 


                        if (exceedsThreshold == false ) 
                        { 
                            gradientValue = 0; 
 
                            // Diagonal Gradient : NW-SE 
                            exceedsThreshold =  
                            CheckThreshold(pixelBuffer,  
                                           sourceOffset - 4 - sourceData.Stride,  
                                           sourceOffset + 4 + sourceData.Stride,  
                                           ref gradientValue, threshold); 


                            if (exceedsThreshold == false ) 
                            {
                                gradientValue = 0; 
 
                                // Diagonal Gradient : NE-SW 
                                exceedsThreshold =  
                                CheckThreshold(pixelBuffer,  
                                               sourceOffset - sourceData.Stride + 4,  
                                               sourceOffset + sourceData.Stride - 4,  
                                               ref gradientValue, threshold); 
                            } 
                        }
                    } 
                }
            }


            resultBuffer[sourceOffset] = (byte)(exceedsThreshold ? 255 : 0); 
            resultBuffer[sourceOffset + 1] = resultBuffer[sourceOffset]; 
            resultBuffer[sourceOffset + 2] = resultBuffer[sourceOffset]; 
            resultBuffer[sourceOffset + 3] = 255; 
        } 
    } 


    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, sourceBitmap.Height); 


    BitmapData resultData = resultBitmap.LockBits(new Rectangle (0, 0, 
                            resultBitmap.Width, resultBitmap.Height), 
                            ImageLockMode.WriteOnly, PixelFormat.Format32bppArgb); 


    Marshal.Copy(resultBuffer, 0, resultData.Scan0, resultBuffer.Length); 
    resultBitmap.UnlockBits(resultData); 


    return resultBitmap; 
}

Rose: Oil Painting, Filter 7, Levels 20, Cartoon Threshold 20

The CartoonFilter extension method serves to combine images generated by the OilPaintFilter and GradientBasedEdgeDetectionFilter methods. The CartoonFilter method being defined as an extension method targets the Bitmap class. In this method pixels detected as forming part of an edge are set to black in Oil Painting filtered images. The definition as follows:

public static Bitmap CartoonFilter(this Bitmap sourceBitmap,
                                       int levels, 
                                       int filterSize, 
                                       byte threshold) 
{
    Bitmap paintFilterImage =  
           sourceBitmap.OilPaintFilter(levels, filterSize);


    Bitmap edgeDetectImage =  
    sourceBitmap.GradientBasedEdgeDetectionFilter(threshold);


    BitmapData paintData = 
               paintFilterImage.LockBits(new Rectangle (0, 0, 
               paintFilterImage.Width, paintFilterImage.Height),
               ImageLockMode.ReadOnly,
               PixelFormat.Format32bppArgb);


    byte[] paintPixelBuffer = new byte[paintData.Stride * 
                                      paintData.Height]; 


    Marshal.Copy(paintData.Scan0, paintPixelBuffer, 0, 
                               paintPixelBuffer.Length); 


    paintFilterImage.UnlockBits(paintData); 


    BitmapData edgeData = 
               edgeDetectImage.LockBits(new Rectangle (0, 0, 
               edgeDetectImage.Width, edgeDetectImage.Height),
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 


    byte[] edgePixelBuffer = new byte[edgeData.Stride * 
                                     edgeData.Height]; 


    Marshal.Copy(edgeData.Scan0, edgePixelBuffer, 0, 
                              edgePixelBuffer.Length); 


    edgeDetectImage.UnlockBits(edgeData); 


    byte[] resultBuffer = new byte [edgeData.Stride * 
                                     edgeData.Height]; 


    for(int k = 0; k + 4 < paintPixelBuffer.Length; k += 4) 
    {
        if (edgePixelBuffer[k] == 255 ||  
            edgePixelBuffer[k + 1] == 255 ||  
            edgePixelBuffer[k + 2] == 255) 
        { 
            resultBuffer[k] = 0; 
            resultBuffer[k + 1] = 0; 
            resultBuffer[k + 2] = 0; 
            resultBuffer[k + 3] = 255; 
        }
        else 
        {
            resultBuffer[k] = paintPixelBuffer[k]; 
            resultBuffer[k + 1] = paintPixelBuffer[k + 1]; 
            resultBuffer[k + 2] = paintPixelBuffer[k + 2]; 
            resultBuffer[k + 3] = 255; 
        }
    }


    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, 
                                     sourceBitmap.Height); 


    BitmapData resultData = 
               resultBitmap.LockBits(new Rectangle (0, 0, 
               resultBitmap.Width, resultBitmap.Height), 
               ImageLockMode.WriteOnly, 
               PixelFormat.Format32bppArgb); 


    Marshal.Copy(resultBuffer, 0, resultData.Scan0, 
                               resultBuffer.Length); 


    resultBitmap.UnlockBits(resultData); 


    return resultBitmap; 
}

Rose: Oil Painting, Filter 9, Levels 25, Cartoon Threshold 25

Sample Images

This article features a number of sample images. All featured images have been licensed allowing for reproduction. The following image files feature a sample images:

Iceberg Rose – Licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license. Attribution: Stan Shebs. Download from Wikipedia
Heidi Klum Rose – Licensed under the Creative Commons Attribution-Share Alike 3.0 Unported, 2.5 Generic, 2.0 Generic and 1.0 Generic license. Download from Wikipedia
White Daisy – Licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license. Download from Wikipedia.
Amber Flush Rose – Licensed under the Creative Commons Attribution-Share Alike 3.0 Unported, 2.5 Generic, 2.0 Generic and 1.0 Generic license. Download from Wikipedia.
Rose Bouquet – Licensed under the Creative Commons Attribution-Share Alike 3.0 Unported, 2.5 Generic, 2.0 Generic and 1.0 Generic license. Download from Wikipedia.
Sunflower – Is in the public domain in the United States because it is a work prepared by an officer or employee of the United States Government as part of that person’s official duties under the terms of Title 17, Chapter 1, Section 105 of the US Code. See Copyright. Download from Wikimedia.
White Rose – Has been released into the public domain by its author, Laitche. This applies worldwide. Download from Wikimedia.

C# How to: Sharpen Edge Detection

Published June 7, 2013 Augmented Reality , C# , Code Samples , Edge Detection , Extension Methods , Graphic Filters , Graphics , How to , Image Arithmetic , Image Convolution , Image Filters , Image Processing , Learn Everyday , Microsoft , New Version , Opensource , Tip , Update 30 Comments
Tags: Augmented Reality, Binary Image, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, Boolean Edge Detection, C#, Change detection, Code Sample, Computer vision, Converting Images, Convolution matrix, Feature extraction, Graphic Filters, Image, Image Edge Detection, Image processing, Image Sharpen, Image Sharpness, Image Transform, Line Detection, Machine vision, Matrix, Pixel Filters

Article Purpose

It is the objective of this article to explore and provide a discussion based in the concept of Edge Detection through means of Image Sharpening. Illustrated are various methods of image sharpening and in addition a Median filter implemented in image noise reduction.

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Using the Sample Application

The sample source code accompanying this article includes a Windows Forms based Sample Application. The concepts illustrated throughout this article can easily be tested and replicated by making use of the Sample Application.

The Sample Application exposes seven main areas of functionality:

Loading input/source images.
Saving image result.
Sharpen Filters
Median Filter Size
Threshold value
Grayscale Source
Mono Output

When using the Sample application users are able to select input/source images from the local file system by clicking the Load Image button. If desired, users may save result images to the local file system by clicking the Save Image button.

The sample source code and sample application implement various methods of Image Sharpening. Each method of image sharpening results in varying degrees of image edge detection. Some methods are more effective than other methods. The image sharpening method being implemented serves as a primary factor influencing edge detection results. The effectiveness of the selected image sharpening method is reliant on the input/source image provided. The sample application implements the following image sharpening methods:

Sharpen5To4
Sharpen7To1
Sharpen9To1
Sharpen12To1
Sharpen24To1
Sharpen48To1
Sharpen10To8
Sharpen11To8
Sharpen821

Image noise is regarded as a common problem relating to image edge detection. Often image noise will be incorrectly detected as forming part of an edge within an image. The sample source code implements a Median filter in order to counter act image noise. The size/intensity of the Median filter applied can be specified via the combobox labelled Median Filter Size.

The Threshold value configured through the sample application’s user interface has a two-fold implementation. In a scenario where output images are created in a black and white format the Threshold value will be implemented to determine whether a pixel should be either black or white. When output images are created as full colour images the Threshold value will be added to each pixel, acting as a bias value.

In some scenarios Image Edge Detection can be achieved more effectively when specifying grayscale format source/input images. The purpose of the checkbox labelled Grayscale Source is to format source/input images in a grayscale format before implementing image edge detection.

The checkbox labelled Mono Output, when selected, has the effect of producing result images in a black and white format.

The image below is a screenshot of the Sharpen Edge Detection sample application in action:

Edge Detection through Image Sharpening

The sample source code performs edge detection on source/input images by means of image sharpening. The steps performed can be broken down to the following items:

If specified, apply a Median filter to the input/source image. A Median filter results in smoothing an image. Image noise can be reduced when implementing a Median Filter. Image smoothing/blurring often results reducing image details/definition. The Median Filter is well suited to smoothing away image noise whilst implementing edge preservation. When performing edge detection the Median filter functions as an ideal method of reducing image noise whilst not negatively impacting edge detection tasks.
If specified, convert the source/input image to grayscale by iterating each pixel that forms part of the image. Each pixel’s colour components are calculated multiplying by factor values: Red x 0.3 Green x 0.59 Blue x 0.11.
Using the specified matrix kernel iterate each pixel forming part of the source/input image, performing image convolution on each pixel colour channel.
If the output image has been specified as Mono, the middle pixel calculated in convolution should be multiplied with the specified factor value. Each colour component should be compared to the specified threshold value and be assigned as either black or white.
If the output image has not been specified as Mono, the middle pixel calculated in convolution should be multiplied with the factor value to which the threshold/bias value should be added. The value of each colour component will be set to the result of subtracting the calculated convolution/filter/bias value from the pixel’s original colour component value. In other words perform image sharpening using convolution applying a factor and bias which should then be subtracted from the original source/input image.

Implementing Sharpen Edge Detection

The sample source code achieves edge detection through image sharpening by implementing three methods: MedianFilter and two overloaded methods titled SharpenEdgeDetect.

The MedianFilter method is defined as an extension method targeting the Bitmap class. The definition as follows:

 public static Bitmap MedianFilter(this Bitmap sourceBitmap, 
                                   int matrixSize) 
{ 
     BitmapData sourceData = 
                sourceBitmap.LockBits(new Rectangle(0, 0, 
                sourceBitmap.Width, sourceBitmap.Height), 
                ImageLockMode.ReadOnly, 
                PixelFormat.Format32bppArgb); 

   
     byte[] pixelBuffer = new byte[sourceData.Stride * 
                                   sourceData.Height]; 

   
     byte[] resultBuffer = new byte[sourceData.Stride * 
                                    sourceData.Height]; 

   
     Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, 
                                pixelBuffer.Length); 

   
     sourceBitmap.UnlockBits(sourceData); 

   
     int filterOffset = (matrixSize - 1) / 2; 
     int calcOffset = 0; 

   
     int byteOffset = 0; 

   
     List<int> neighbourPixels = new List<int>(); 
     byte[] middlePixel; 

   
     for (int offsetY = filterOffset; offsetY < 
         sourceBitmap.Height - filterOffset; offsetY++) 
     { 
         for (int offsetX = filterOffset; offsetX < 
             sourceBitmap.Width - filterOffset; offsetX++) 
         { 
             byteOffset = offsetY * 
                          sourceData.Stride + 
                          offsetX * 4; 

   
             neighbourPixels.Clear(); 

   
             for (int filterY = -filterOffset; 
                 filterY <= filterOffset; filterY++) 
             {
                 for (int filterX = -filterOffset; 
                     filterX <= filterOffset; filterX++) 
                 {

   
                     calcOffset = byteOffset + 
                                  (filterX * 4) + 
                                  (filterY * sourceData.Stride); 

   
                     neighbourPixels.Add(BitConverter.ToInt32( 
                                      pixelBuffer, calcOffset)); 
                 } 
             } 

   
             neighbourPixels.Sort(); 
  
             middlePixel = BitConverter.GetBytes( 
                                neighbourPixels[filterOffset]); 

   
             resultBuffer[byteOffset] = middlePixel[0]; 
             resultBuffer[byteOffset + 1] = middlePixel[1]; 
             resultBuffer[byteOffset + 2] = middlePixel[2]; 
             resultBuffer[byteOffset + 3] = middlePixel[3]; 
         } 
     }

   
     Bitmap resultBitmap = new Bitmap (sourceBitmap.Width, 
                                      sourceBitmap.Height); 

   
     BitmapData resultData = 
                resultBitmap.LockBits(new Rectangle  (0, 0, 
                resultBitmap.Width, resultBitmap.Height), 
                ImageLockMode.WriteOnly, 
                PixelFormat.Format32bppArgb); 

   
     Marshal.Copy(resultBuffer, 0, resultData.Scan0, 
                                resultBuffer.Length); 

   
     resultBitmap.UnlockBits(resultData); 

   
     return resultBitmap; 
}

The public implementation of the SharpenEdgeDetect extension method has the purpose of translating user specified options into the relevant method calls to the private implementation of the SharpenEdgeDetect extension method. The public implementation of the SharpenEdgeDetect method as follows:

public static Bitmap SharpenEdgeDetect(this Bitmap sourceBitmap, 
                                            SharpenType sharpen, 
                                                   int bias = 0, 
                                         bool grayscale = false, 
                                              bool mono = false, 
                                       int medianFilterSize = 0) 
{ 
    Bitmap resultBitmap = null; 

   
    if (medianFilterSize == 0) 
    {
        resultBitmap = sourceBitmap; 
    } 
    else 
    {
        resultBitmap =  
        sourceBitmap.MedianFilter(medianFilterSize); 
    } 

       
    switch (sharpen) 
    { 
        case SharpenType.Sharpen7To1: 
            { 
                resultBitmap =  
                resultBitmap.SharpenEdgeDetect( 
                             Matrix.Sharpen7To1,  
                             1.0, bias, grayscale, mono); 
            } break; 
        case SharpenType.Sharpen9To1: 
            { 
                resultBitmap =  
                    resultBitmap.SharpenEdgeDetect( 
                                Matrix.Sharpen9To1,  
                                1.0, bias, grayscale, mono); 
            } break; 
        case SharpenType.Sharpen12To1: 
            { 
                resultBitmap =  
                    resultBitmap.SharpenEdgeDetect( 
                                Matrix.Sharpen12To1,  
                                1.0, bias, grayscale, mono); 
            } break; 
        case SharpenType.Sharpen24To1: 
            {
                resultBitmap =  
                resultBitmap.SharpenEdgeDetect( 
                            Matrix.Sharpen24To1,  
                            1.0, bias, grayscale, mono); 
            } break; 
        case SharpenType.Sharpen48To1: 
            { 
                resultBitmap =  
                resultBitmap.SharpenEdgeDetect( 
                            Matrix.Sharpen48To1,  
                            1.0, bias, grayscale, mono); 
            } break; 
        case SharpenType.Sharpen5To4: 
            { 
                resultBitmap =  
                resultBitmap.SharpenEdgeDetect( 
                            Matrix.Sharpen5To4,  
                            1.0, bias, grayscale, mono); 
            } break; 
        case SharpenType.Sharpen10To8: 
            { 
                resultBitmap =  
                resultBitmap.SharpenEdgeDetect( 
                            Matrix.Sharpen10To8,  
                            1.0, bias, grayscale, mono); 
            } break; 
        case SharpenType.Sharpen11To8: 
            { 
                resultBitmap =  
                resultBitmap.SharpenEdgeDetect( 
                            Matrix.Sharpen11To8,  
                            3.0 / 1.0, bias, grayscale, mono); 
            } break; 
        case SharpenType.Sharpen821: 
            { 
                resultBitmap =  
                resultBitmap.SharpenEdgeDetect( 
                            Matrix.Sharpen821,  
                            8.0 / 1.0, bias, grayscale, mono); 
            } break; 
    } 

   
    return resultBitmap; 
}

The Matrix class provides the definition of static pre-defined matrix kernel values. The definition as follows:

public static class Matrix   
{
    public static double[,] Sharpen7To1 
    {
        get   
        { 
            return new double[,]   
            {  { 1,  1,  1, },  
               { 1, -7,  1, },   
               { 1,  1,  1, }, }; 
        }  
    }  

   
    public static double[,] Sharpen9To1 
    {  
        get   
        {  
            return new double[,]   
            {  { -1, -1, -1, },  
               { -1,  9, -1, },   
               { -1, -1, -1, }, }; 
        }  
    }  

   
    public static double[,] Sharpen12To1 
    {  
        get   
        {  
            return new double[,]   
            {  { -1, -1, -1, },  
               { -1, 12, -1, },   
               { -1, -1, -1, }, }; 
        }  
    }  

   
    public static double[,] Sharpen24To1 
    {  
        get   
        {  
            return new double[,]   
            {  { -1, -1, -1, -1, -1, },  
               { -1, -1, -1, -1, -1, },  
               { -1, -1, 24, -1, -1, },  
               { -1, -1, -1, -1, -1, },  
               { -1, -1, -1, -1, -1, }, }; 
        }  
    }  

   
    public static double[,] Sharpen48To1 
    {  
        get   
        {  
            return new double[,]   
            {  {  -1, -1, -1, -1, -1, -1, -1, },  
               {  -1, -1, -1, -1, -1, -1, -1, },  
               {  -1, -1, -1, -1, -1, -1, -1, },  
               {  -1, -1, -1, 48, -1, -1, -1, },  
               {  -1, -1, -1, -1, -1, -1, -1, },  
               {  -1, -1, -1, -1, -1, -1, -1, },  
               {  -1, -1, -1, -1, -1, -1, -1, }, }; 
        }  
    }  

   
    public static double[,] Sharpen5To4 
    {  
        get   
        {  
            return new double[,]  
            {  {  0, -1,  0, },  
               { -1,  5, -1, },  
               {  0, -1,  0, }, }; 
        }  
    }  

   
    public static double[,] Sharpen10To8 
    {  
        get   
        {  
            return new double[,]  
            {  {  0, -2,  0, },  
               { -2, 10, -2, },  
               {  0, -2,  0, }, }; 
        }  
    }  

   
    public static double[,] Sharpen11To8 
    {  
        get   
        {  
            return new double[,]   
            {  {  0, -2,  0, },  
               { -2, 11, -2, },  
               {  0, -2,  0, }, }; 
        }  
    }  

   
    public static double[,] Sharpen821 
    {  
        get   
        {  
            return new double[,]   
            {  {  -1, -1, -1, -1, -1, },  
               {  -1,  2,  2,  2, -1, },  
               {  -1,  2,  8,  2,  1, }, 
               {  -1,  2,  2,  2, -1, }, 
               {  -1, -1, -1, -1, -1, }, }; 
        }  
    }  
}

The private implementation of the SharpenEdgeDetect extension method performs image sharpening through convolution and then performs image subtraction. The definition as follows:

private static Bitmap SharpenEdgeDetect(this Bitmap sourceBitmap, 
                                          double[,] filterMatrix, 
                                               double factor = 1, 
                                                    int bias = 0, 
                                          bool grayscale = false, 
                                               bool mono = false) 
{ 
    BitmapData sourceData = sourceBitmap.LockBits(new Rectangle(0, 0, 
                             sourceBitmap.Width, sourceBitmap.Height), 
                                               ImageLockMode.ReadOnly, 
                                         PixelFormat.Format32bppArgb); 

   
    byte[] pixelBuffer = new byte[sourceData.Stride * sourceData.Height]; 
    byte[] resultBuffer = new byte[sourceData.Stride * sourceData.Height]; 

   
    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, pixelBuffer.Length); 
    sourceBitmap.UnlockBits(sourceData); 

   
    if (grayscale == true) 
    { 
        for (int pixel = 0; pixel < pixelBuffer.Length; pixel += 4) 
        { 
            pixelBuffer[pixel] =  
                (byte)(pixelBuffer[pixel] * 0.11f); 

   
            pixelBuffer[pixel + 1] =  
                (byte)(pixelBuffer[pixel + 1] * 0.59f); 

   
            pixelBuffer[pixel + 2] =  
                (byte)(pixelBuffer[pixel + 2] * 0.3f); 
        } 
    }

   
    double blue = 0.0; 
    double green = 0.0; 
    double red = 0.0; 

   
    int filterWidth = filterMatrix.GetLength(1); 
    int filterHeight = filterMatrix.GetLength(0); 

   
    int filterOffset = (filterWidth - 1) / 2; 
    int calcOffset = 0; 

   
    int byteOffset = 0; 

   
    for (int offsetY = filterOffset; offsetY < 
        sourceBitmap.Height - filterOffset; offsetY++) 
    { 
        for (int offsetX = filterOffset; offsetX < 
            sourceBitmap.Width - filterOffset; offsetX++) 
        {
            blue = 0; 
            green = 0; 
            red = 0; 

   
            byteOffset = offsetY * 
                         sourceData.Stride + 
                         offsetX * 4; 

   
            for (int filterY = -filterOffset; 
                filterY <= filterOffset; filterY++) 
            {
                for (int filterX = -filterOffset; 
                    filterX <= filterOffset; filterX++) 
                {
                    calcOffset = byteOffset + 
                                 (filterX * 4) + 
                                 (filterY * sourceData.Stride); 

   
                    blue += (double  )(pixelBuffer[calcOffset]) * 
                            filterMatrix[filterY + filterOffset, 
                                                filterX + filterOffset]; 

   
                    green += (double  )(pixelBuffer[calcOffset + 1]) * 
                             filterMatrix[filterY + filterOffset, 
                                                filterX + filterOffset]; 

   
                    red += (double  )(pixelBuffer[calcOffset + 2]) * 
                           filterMatrix[filterY + filterOffset, 
                                              filterX + filterOffset]; 
                }
            }

   
            if (mono == true) 
            { 
                blue = resultBuffer[byteOffset] - factor * blue; 
                green = resultBuffer[byteOffset + 1] - factor * green; 
                red = resultBuffer[byteOffset + 2] - factor * red; 

   
                blue = (blue > bias ? 255 : 0); 

   
                green = (blue > bias ? 255 : 0); 

   
                red = (blue > bias ? 255 : 0); 
            }
            else 
            {
                blue = resultBuffer[byteOffset] -  
                       factor * blue + bias; 

   
                green = resultBuffer[byteOffset + 1] -  
                        factor * green + bias; 

   
                red = resultBuffer[byteOffset + 2] -  
                      factor * red + bias; 

   
                blue = (blue > 255 ? 255 : 
                       (blue < 0 ? 0 : 
                        blue)); 

   
                green = (green > 255 ? 255 : 
                        (green < 0 ? 0 : 
                         green)); 

   
                red = (red > 255 ? 255 : 
                      (red < 0 ? 0 : 
                       red)); 
            }  

   
            resultBuffer[byteOffset] = (byte)(blue); 
            resultBuffer[byteOffset + 1] = (byte)(green); 
            resultBuffer[byteOffset + 2] = (byte)(red); 
            resultBuffer[byteOffset + 3] = 255; 
        }
    }

   
    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, sourceBitmap.Height); 
 
    BitmapData resultData = resultBitmap.LockBits(new Rectangle(0, 0, 
                             resultBitmap.Width, resultBitmap.Height), 
                                              ImageLockMode.WriteOnly, 
                                         PixelFormat.Format32bppArgb); 

   
    Marshal.Copy(resultBuffer, 0, resultData.Scan0, resultBuffer.Length); 
    resultBitmap.UnlockBits(resultData); 

   
    return resultBitmap; 
}

Sample Images

The sample image used in this article is in the public domain because its copyright has expired. This applies to Australia, the European Union and those countries with a copyright term of life of the author plus 70 years. The original image can be downloaded from Wikipedia.

The Original Image

Sharpen5To4, Median 0, Threshold 0

Sharpen5To4, Median 0, Threshold 0, Mono

Sharpen7To1, Median 0, Threshold 0

Sharpen7To1, Median 0, Threshold 0, Mono

Sharpen9To1, Median 0, Threshold 0

Sharpen9To1, Median 0, Threshold 0, Mono

Sharpen10To8, Median 0, Threshold 0

Sharpen10To8, Median 0, Threshold 0, Mono

Sharpen11To8, Median 0, Threshold 0

Sharpen11To8, Median 0, Threshold 0, Grayscale, Mono

Sharpen12To1, Median 0, Threshold 0

Sharpen12To1, Median 0, Threshold 0, Mono

Sharpen24To1, Median 0, Threshold 0

Sharpen24To1, Median 0, Threshold 0, Grayscale, Mono

Sharpen24To1, Median 0, Threshold 0, Mono

Sharpen24To1, Median 0, Threshold 21, Grayscale, Mono

Sharpen48To1, Median 0, Threshold 0

Sharpen48To1, Median 0, Threshold 0, Grayscale, Mono

Sharpen48To1, Median 0, Threshold 0, Mono

Sharpen48To1, Median 0, Threshold 226, Mono

C# How to: Image Cartoon Effect

Published June 2, 2013 Augmented Reality , C# , Code Samples , Edge Detection , Extension Methods , Graphic Filters , Graphics , How to , Image Arithmetic , Image Convolution , Image Filters , Image Processing , Microsoft , Morphology Filters , New Version , Opensource , Update 22 Comments
Tags: Animation Effect, Augmented Reality, Binary Image, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, Boolean Edge Detection, C#, Cartoon Effect, Change detection, Code Sample, Computer vision, Converting Images, Convolution filters, Convolution Kernel, Convolution matrix, Edge Masks, Feature extraction, Gradient Edge Detection, Graphic Filters, Image, Image Edge Detection, Image Gradient, Image Morphology, Image processing, Image Sharpen, Image Sharpness, Image Transform, Line Detection, Machine vision, Matrix, Pixel Filters

Article purpose

In this article we explore the tasks related to creating a Cartoon Effect from images which reflect real world non-animated scenarios. When applying a Cartoon Effect it becomes possible with relative ease to create images appearing to have originated from a drawing/animation.

Cartoon version of Steve Ballmer: Low Pass 3×3, Threshold 65.

Sample source code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

CPU: Gaussian 7×7, Threshold 84

Using the Sample Application

A Sample Application has been included as part of the sample source code accompanying this article. The Sample Application is a Windows Forms based application which enables a user to specify source/input images, apply various methods of implementing the Cartoon Effect. In addition users are able to save generated images to the local file system.

When using the Sample Application click the Load Image button to load image files from the local file system. On the right-hand side of the Sample application’s user interface, users are provided with two configuration options: Smoothing Filter and Threshold.

Rose: Gaussian 3×3 Threshold 28.

In this article and sample source code image detail and definition can be reduced through means of image smoothing filters. Several image smoothing options are available to the user, the following section serves as a discussion of each option.

None – When specifying the Smoothing Filter option None, no image smoothing operations will be performed on source/input images.

Gaussian 3×3 – Gaussian blur filters can be very effective at removing image noise, smoothing an image background, whilst still preserving the edges expressed in the sample/input image. A Convolution matrix/kernel of 3×3 dimensions result in slight image blurring.

Gaussian 5×5 – A Gaussian blur operation being implemented by making use of a convolution matrix/kernel defined with dimensions of 5×5. A slightly larger kernel results in an increased level of image blurring being expressed by output images. A greater level of image blurring equates to a larger degree of image noise reduction/removal.

Rose: Gaussian 7×7 Threshold 48.

Gaussian 7×7 – As can be expected when specifying a convolution matrix/kernel conforming to 7×7 size dimension an even more intense level of image blurring can be detected when looking at result images. Notice how increased levels of image blurring negatively affects the process of edge detection. Consider the following: In a scenario where too many elements are being detected as part of an edge as a result of image noise, specifying a higher level of image blurring should reduce edges being detected. The reasoning can be explained in terms of image blurring reducing image definition/detail, higher levels of image blurring will thus result in a greater level of image detail/definition reduction. Lower definition images are less likely to express the same level of detected edges when compared to higher definition images.

CPU: Median 3×3, Threshold 96.

Median 3×3 – When applying a Median filter to an image the resulting image should express a lesser degree of image noise. In other words, the Median filter can be considered as well suited to performing noise reduction. Also note that a Median filter under certain conditions has the ability to preserve the edges contained in an image. In the following section we explore the importance of edge detection in achieving a Cartoon Effect. Important concepts to take note of: The Median Filter when implemented on an image performs noise reduction whilst preserving image edges. In relation, edge detection represents a core concept/task when creating a Cartoon Effect. The Median Filter’s edge preservation property compliments the process of edge detection. When an image contains a low level of image noise the Median 3×3 Filter could be considered.

Median 5×5 – The 5×5 dimension implementation of the Median Filter result in producing images which exhibit a higher degree of smoothing and a lesser expression of image noise. If the 3×3 Median Filter fails to provide adequate levels of image smoothing and noise reduction the 5×5 Median Filter could be implemented.

Cartoon version of Steve Ballmer: Sharpen 3×3, Threshold 80.

Median 7×7 – The last Median Filter implemented by the sample source code conforms to a 7×7 size dimension. This filter variation results in a high level of image noise reduction. The trade off to more effective noise reduction will be expressed in result images appearing extremely smooth, in some scenarios perhaps overly so.

Mean 3×3 – The Mean Filter provides a different implementation towards achieving image smoothing and noise reduction.

Mean 5×5 – The 5×5 dimension Mean Filter variation serves as a more intense version of the Mean 3×3 Filter. Depending on the level of image noise and type of image noise a Mean Filter could prove a more efficient implementation in comparison to a Median Filter.

Low Pass 3×3 – In much the same fashion as Median and Mean Filters, a Low Pass Filter achieves images smoothing and noise reduction. Notice when comparing Median, Mean and Low Pass Filtering, the differences observed in output results are only expressed as slight differences. The most effective filter to apply should be seen as as being dependent on the input/source image characteristics.

CPU: Gaussian 3×3, Threshold 92.

Low Pass 5×5 – This filter variation being of a larger dimension serves as a more intense implementation of the Low Pass 3×3 Filter.

Sharpen 3×3 – In certain scenarios input/source images may already be smoothed/blurred to such an extent where the edge detection process performs below expectation. Edge detection can be improved when applying a image Sharpen filter.

Threshold values specified by the user through the user interface serves the purpose of enabling the user to finely control the extent/intensity of edges being detected. Implementing a higher Threshold value will have the result of less edges being detected. In order to reduce the level of image noise being detected as false edges the Threshold value should be increased. When too few edges are being detected the Threshold value should be decreased.

The following image is a screenshot of the Image Cartoon Effect Sample Application in action:

Explanation of the Cartoon Effect

The Cartoon Effect can be characterised as an image filter producing result images which appear similar to input/source images with the exception of having an animated appearance.

The Cartoon Effect consists of reducing image detail/definition whilst at the same instance performing edge detection. The resulting smoothed image and the edges detected in the source/input image should be combined, where detected edges are being expressed in the colour black. The final image reflects an appearance similar to that of an animated/artist drawn image.

Various methods of reducing image detail/definition are supported in the sample source code. Most methods consist of implementing image smoothing. The following configurable methods are implemented:

None
Gaussian 3×3
Gaussian 5×5
Gaussian 7×7
Median 3×3
Median 5×5
Median 7×7
Median 9×9
Mean 3×3
Mean 5×5
Low Pass 3×3
Low Pass 5×5
Sharpen 3×3

Rose: Mean 5×5 Threshold 37.

All of the filter methods listed above are implemented by means of Image Convolution. The size dimensions listed for each filter option relates to the dimension of the kernel/matrix being implemented by a filter.

When applying a filter, the intensity/extent will be determined by the size dimensions of the Convolution kernel/matrix implemented. Smaller kernel/matrix dimensions result in a filter being applied to a lesser extent. Larger kernel/matrix dimensions will result in the filter effect being more evident, being applied to a greater extent. Image noise reduction will be achieved when implementing a filter.

The Sample Source code implements Gradient Based Edge Detection using the original source/input image, therefore not being influenced by any smoothing operations. I have published an in-depth article on the topic of Gradient Based Edge Detection which can be located here: C# How to: Gradient Based Edge Detection.

Rose: Median 3×3 Threshold 37.

The Sample source code implements Gradient Based Edge Detection by means of iterating each pixel that forms part of the sample/input image. Whilst iterating image pixels the sample code calculate various gradients from the current pixel’s neighbouring pixels, on a per colour component basis (Red, Green and Blue). Referring to neighbouring pixels, calculations include the value of each of the surrounding pixels in regards to the pixel currently being iterated. Neighbouring pixel calculations are better know as kernel/window/matrix operations.

Note: Do not confuse image convolution and the method in which we iterate and calculate gradients. Although both methods have various aspects in common, image convolution is regarded as linear filter processing, whereas our method qualifies as a non-linear filter.

We calculate various gradients, which is to be compared against the user specified global threshold value. If a calculated gradient value exceeds the value of the user specified threshold the pixel currently being iterated will be considered as part of an edge.

The first gradients to be calculated involves the pixel directly above, below, left and right of the current pixel. A gradient will be calculated for each colour component. The gradient values being calculated can be considered as an indicator reflecting the rate of change. If the sum total of the calculated gradients exceed that of the global threshold the pixel will be considered as forming part an edge.

When the comparison of the threshold value and the total gradient value reflects in favour of the threshold the following set of gradients will be calculated. This process of calculating gradients will continue either until a gradient value exceeds the threshold or all gradients have been calculated.

If a pixel was detected as forming part of an edge, the pixel’s colour will be set to black. In the case of non-edge pixels, the original colour components from the source/input image will be used in setting the current pixel’s value.

Rose: Gaussian 3×3 Threshold 28

Implementing Cartoon Effects

The sample source code implementation can be divided into five distinct components: Cartoon Effect Filter, smoothing helper method, Median Filter implementation, Convolution implementation and the collection of pre-defined matrix/kernel values.

The sample source code defines the MedianFilter extension method targeting the Bitmap class. The following code snippet provides the definition:

 public static Bitmap MedianFilter(this Bitmap sourceBitmap, 
                                   int matrixSize) 
{ 
     BitmapData sourceData = 
                sourceBitmap.LockBits(new Rectangle(0, 0, 
                sourceBitmap.Width, sourceBitmap.Height), 
                ImageLockMode.ReadOnly, 
                PixelFormat.Format32bppArgb); 

   
     byte[] pixelBuffer = new byte[sourceData.Stride * 
                                   sourceData.Height]; 

   
     byte[] resultBuffer = new byte[sourceData.Stride * 
                                    sourceData.Height]; 

   
     Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, 
                                pixelBuffer.Length); 

   
     sourceBitmap.UnlockBits(sourceData); 

   
     int filterOffset = (matrixSize - 1) / 2; 
     int calcOffset = 0; 

   
     int byteOffset = 0; 

   
     List<int> neighbourPixels = new List<int>(); 
     byte[] middlePixel; 

   
     for (int offsetY = filterOffset; offsetY < 
         sourceBitmap.Height - filterOffset; offsetY++) 
     { 
         for (int offsetX = filterOffset; offsetX < 
             sourceBitmap.Width - filterOffset; offsetX++) 
         { 
             byteOffset = offsetY * 
                          sourceData.Stride + 
                          offsetX * 4; 

   
             neighbourPixels.Clear(); 

   
             for (int filterY = -filterOffset; 
                 filterY <= filterOffset; filterY++) 
             {
                 for (int filterX = -filterOffset; 
                     filterX <= filterOffset; filterX++) 
                 {

   
                     calcOffset = byteOffset + 
                                  (filterX * 4) + 
                                  (filterY * sourceData.Stride); 

   
                     neighbourPixels.Add(BitConverter.ToInt32( 
                                      pixelBuffer, calcOffset)); 
                 } 
             } 

   
             neighbourPixels.Sort(); 
  
             middlePixel = BitConverter.GetBytes( 
                                neighbourPixels[filterOffset]); 

   
             resultBuffer[byteOffset] = middlePixel[0]; 
             resultBuffer[byteOffset + 1] = middlePixel[1]; 
             resultBuffer[byteOffset + 2] = middlePixel[2]; 
             resultBuffer[byteOffset + 3] = middlePixel[3]; 
         } 
     }

   
     Bitmap resultBitmap = new Bitmap (sourceBitmap.Width, 
                                      sourceBitmap.Height); 

   
     BitmapData resultData = 
                resultBitmap.LockBits(new Rectangle  (0, 0, 
                resultBitmap.Width, resultBitmap.Height), 
                ImageLockMode.WriteOnly, 
                PixelFormat.Format32bppArgb); 

   
     Marshal.Copy(resultBuffer, 0, resultData.Scan0, 
                                resultBuffer.Length); 

   
     resultBitmap.UnlockBits(resultData); 

   
     return resultBitmap; 
}

The SmoothingFilterType enumeration, defined by the sample source code, serves as a strongly typed definition of a the collection of implemented smoothing filters. The definition as follows:

 public enum SmoothingFilterType  
 {
     None, 
     Gaussian3x3, 
     Gaussian5x5, 
     Gaussian7x7, 
     Median3x3, 
     Median5x5, 
     Median7x7, 
     Median9x9, 
     Mean3x3, 
     Mean5x5, 
     LowPass3x3, 
     LowPass5x5, 
     Sharpen3x3, 
 }

The Matrix class contains the definition of all the two dimensional kernel/matrix values implemented when performing image convolution. The definition as follows:

public static class Matrix 
{ 
    public static double[,] Gaussian3x3 
    { 
        get 
        {
            return new double[,]   
             { { 1, 2, 1, },  
               { 2, 4, 2, },  
               { 1, 2, 1, }, }; 
        } 
    }
 
    public static double[,] Gaussian5x5 
    {
        get 
        { 
            return new double[,]   
             { { 2, 04, 05, 04, 2  },  
               { 4, 09, 12, 09, 4  },  
               { 5, 12, 15, 12, 5  }, 
               { 4, 09, 12, 09, 4  }, 
               { 2, 04, 05, 04, 2  }, }; 
        } 
    } 
 
    public static double[,] Gaussian7x7 
    {
        get 
        { 
            return new double[,]   
             { { 1,  1,  2,  2,  2,  1,  1, },  
               { 1,  2,  2,  4,  2,  2,  1, },  
               { 2,  2,  4,  8,  4,  2,  2, },  
               { 2,  4,  8, 16,  8,  4,  2, },  
               { 2,  2,  4,  8,  4,  2,  2, },  
               { 1,  2,  2,  4,  2,  2,  1, },  
               { 1,  1,  2,  2,  2,  1,  1, }, }; 
        } 
    } 
 
    public static double[,] Mean3x3 
    { 
        get 
        { 
            return new double[,]   
             { { 1, 1, 1, },  
               { 1, 1, 1, },  
               { 1, 1, 1, }, }; 
        } 
    } 
 
    public static double[,] Mean5x5 
    { 
        get 
        { 
            return new double[,]   
             { { 1, 1, 1, 1, 1, },  
               { 1, 1, 1, 1, 1, },  
               { 1, 1, 1, 1, 1, },  
               { 1, 1, 1, 1, 1, },  
               { 1, 1, 1, 1, 1, }, }; 
        } 
    } 
 
    public static double [,] LowPass3x3 
    { 
        get 
        { 
            return new double [,]   
             { { 1, 2, 1, },  
               { 2, 4, 2, },   
               { 1, 2, 1, }, }; 
        }
    } 
 
    public static double[,] LowPass5x5 
    { 
        get 
        { 
            return new double[,]   
             { { 1, 1,  1, 1, 1, },  
               { 1, 4,  4, 4, 1, },  
               { 1, 4, 12, 4, 1, },  
               { 1, 4,  4, 4, 1, },  
               { 1, 1,  1, 1, 1, }, }; 
        }
    }
 
    public static double[,] Sharpen3x3 
    { 
        get 
         {
            return new double[,]   
             { { -1, -2, -1, },  
               {  2,  4,  2, },   
               {  1,  2,  1, }, }; 
         }
    } 
}

Rose: Low Pass 3×3 Threshold 61

The SmoothingFilter extension method targets the Bitmap class. This method implements image convolution. The primary task performed by the SmoothingFilter extension method involves translating convolution filter options into the correct method calls. The definition as follows:

public static Bitmap SmoothingFilter(this Bitmap sourceBitmap, 
                            SmoothingFilterType smoothFilter = 
                            SmoothingFilterType.None) 
 {
    Bitmap inputBitmap = null; 

   
    switch (smoothFilter) 
    { 
        case SmoothingFilterType.None: 
            {
                inputBitmap = sourceBitmap; 
            } break; 
        case SmoothingFilterType.Gaussian3x3: 
            {
                inputBitmap = sourceBitmap.ConvolutionFilter( 
                           Matrix.Gaussian3x3, 1.0 / 16.0, 0); 
            } break; 
        case SmoothingFilterType.Gaussian5x5: 
            { 
                inputBitmap = sourceBitmap.ConvolutionFilter( 
                          Matrix.Gaussian5x5, 1.0 / 159.0, 0); 
            } break; 
        case SmoothingFilterType.Gaussian7x7: 
            { 
                inputBitmap = sourceBitmap.ConvolutionFilter( 
                          Matrix.Gaussian7x7, 1.0 / 136.0, 0); 
            } break; 
        case SmoothingFilterType.Median3x3: 
            { 
                inputBitmap = sourceBitmap.MedianFilter(3); 
            } break; 
        case SmoothingFilterType.Median5x5: 
            { 
                inputBitmap = sourceBitmap.MedianFilter(5); 
            } break; 
        case SmoothingFilterType.Median7x7: 
            { 
                inputBitmap = sourceBitmap.MedianFilter(7); 
            } break; 
        case SmoothingFilterType.Median9x9: 
            { 
                inputBitmap = sourceBitmap.MedianFilter(9); 
            } break; 
        case SmoothingFilterType.Mean3x3: 
            { 
                inputBitmap = sourceBitmap.ConvolutionFilter( 
                              Matrix.Mean3x3, 1.0 / 9.0, 0); 
            } break; 
        case SmoothingFilterType.Mean5x5: 
            { 
                inputBitmap = sourceBitmap.ConvolutionFilter( 
                              Matrix.Mean5x5, 1.0 / 25.0, 0); 
            } break; 
        case SmoothingFilterType.LowPass3x3: 
            { 
                inputBitmap = sourceBitmap.ConvolutionFilter( 
                              Matrix.LowPass3x3, 1.0 / 16.0, 0); 
            } break; 
        case SmoothingFilterType.LowPass5x5: 
            { 
                inputBitmap = sourceBitmap.ConvolutionFilter( 
                              Matrix.LowPass5x5, 1.0 / 60.0, 0); 
            } break; 
        case SmoothingFilterType.Sharpen3x3: 
            { 
                inputBitmap = sourceBitmap.ConvolutionFilter( 
                              Matrix.Sharpen3x3, 1.0 / 8.0, 0); 
            } break; 
    } 

   
    return inputBitmap; 
}

The ConvolutionFilter extension method which targets the Bitmap class implements image convolution. The definition as follows:

private static Bitmap ConvolutionFilter(this Bitmap sourceBitmap, 
                                          double[,] filterMatrix, 
                                               double factor = 1, 
                                                    int bias = 0) 
{ 
    BitmapData sourceData = sourceBitmap.LockBits(new Rectangle(0, 0, 
                             sourceBitmap.Width, sourceBitmap.Height), 
                                               ImageLockMode.ReadOnly, 
                                         PixelFormat.Format32bppArgb); 

   
    byte[] pixelBuffer = new byte[sourceData.Stride * sourceData.Height]; 
    byte[] resultBuffer = new byte[sourceData.Stride * sourceData.Height]; 

   
    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, pixelBuffer.Length); 
    sourceBitmap.UnlockBits(sourceData); 

   
    double blue = 0.0; 
    double green = 0.0; 
    double red = 0.0; 

   
    int filterWidth = filterMatrix.GetLength(1); 
    int filterHeight = filterMatrix.GetLength(0); 

   
    int filterOffset = (filterWidth - 1) / 2; 
    int calcOffset = 0; 

   
    int byteOffset = 0; 

   
    for (int offsetY = filterOffset; offsetY < 
        sourceBitmap.Height - filterOffset; offsetY++) 
     { 
        for (int offsetX = filterOffset; offsetX < 
            sourceBitmap.Width - filterOffset; offsetX++) 
         { 
            blue = 0; 
            green = 0; 
            red = 0; 

   
            byteOffset = offsetY * 
                         sourceData.Stride + 
                         offsetX * 4; 

   
            for (int filterY = -filterOffset; 
                filterY <= filterOffset; filterY++) 
             { 
                for (int filterX = -filterOffset; 
                    filterX <= filterOffset; filterX++) 
                 { 

   
                    calcOffset = byteOffset + 
                                 (filterX * 4) + 
                                 (filterY * sourceData.Stride); 

   
                    blue += (double)(pixelBuffer[calcOffset]) * 
                            filterMatrix[filterY + filterOffset, 
                                                filterX + filterOffset]; 

   
                    green += (double)(pixelBuffer[calcOffset + 1]) * 
                             filterMatrix[filterY + filterOffset, 
                                                filterX + filterOffset]; 

   
                    red += (double)(pixelBuffer[calcOffset + 2]) * 
                           filterMatrix[filterY + filterOffset, 
                                              filterX + filterOffset]; 
                 } 
             }

   
            blue = factor * blue + bias; 
            green = factor * green + bias; 
            red = factor * red + bias; 

   
            blue = (blue > 255 ? 255 : 
                   (blue < 0 ? 0 : 
                    blue)); 

   
            green = (green > 255 ? 255 : 
                    (green < 0 ? 0 : 
                     green)); 

   
            red = (red > 255 ? 255 : 
                  (red < 0 ? 0 : 
                   red));  

   
            resultBuffer[byteOffset] = (byte)(blue); 
            resultBuffer[byteOffset + 1] = (byte)(green); 
            resultBuffer[byteOffset + 2] = (byte)(red); 
            resultBuffer[byteOffset + 3] = 255; 
         } 
     }

   
    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, sourceBitmap.Height); 

   
    BitmapData resultData = resultBitmap.LockBits(new Rectangle (0, 0, 
                             resultBitmap.Width, resultBitmap.Height), 
                                              ImageLockMode .WriteOnly, 
                                         PixelFormat .Format32bppArgb); 

   
    Marshal.Copy(resultBuffer, 0, resultData.Scan0, resultBuffer.Length); 
    resultBitmap.UnlockBits(resultData); 

   
    return resultBitmap; 
}

Cartoon version of Steve Ballmer: Sharpen 3×3 Threshold 80

The CartoonEffectFilter extension method targets the Bitmap class. This method defines all the tasks required in order to implement a Cartoon Filter. From an implementation point of view, consuming code is only required to invoke this method, no other additional method calls are required. The definition as follows:

public static Bitmap CartoonEffectFilter( 
                                this Bitmap sourceBitmap, 
                                byte threshold = 0, 
                                SmoothingFilterType smoothFilter  
                                = SmoothingFilterType.None) 
{ 
    sourceBitmap = sourceBitmap.SmoothingFilter(smoothFilter); 

       
    BitmapData sourceData = 
               sourceBitmap.LockBits(new Rectangle (0, 0, 
               sourceBitmap.Width, sourceBitmap.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 

   
    byte[] pixelBuffer = new byte[sourceData.Stride * 
                                  sourceData.Height]; 

   
    byte[] resultBuffer = new byte[sourceData.Stride * 
                                   sourceData.Height]; 

   
    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, 
                               pixelBuffer.Length); 

   
    sourceBitmap.UnlockBits(sourceData); 

   
    int byteOffset = 0; 
    int blueGradient, greenGradient, redGradient = 0; 
    double blue = 0, green = 0, red = 0; 

   
    bool exceedsThreshold = false; 

   
    for (int offsetY = 1; offsetY <  
         sourceBitmap.Height - 1; offsetY++) 
    {
        for (int offsetX = 1; offsetX < 
            sourceBitmap.Width - 1; offsetX++) 
        { 
            byteOffset = offsetY * sourceData.Stride + 
                         offsetX * 4; 

   
            blueGradient =  
            Math.Abs(pixelBuffer[byteOffset - 4] - 
            pixelBuffer[byteOffset + 4]); 

   
            blueGradient +=  
            Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
            pixelBuffer[byteOffset + sourceData.Stride]); 

               
            byteOffset++; 

   
            greenGradient =  
            Math.Abs(pixelBuffer[byteOffset - 4] -  
            pixelBuffer[byteOffset + 4]); 

   
            greenGradient +=  
            Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
            pixelBuffer[byteOffset + sourceData.Stride]); 

   
            byteOffset++; 

   
            redGradient =  
            Math.Abs(pixelBuffer[byteOffset - 4] -  
            pixelBuffer[byteOffset + 4]); 

   
            redGradient +=  
            Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
            pixelBuffer[byteOffset + sourceData.Stride]); 

               
            if (blueGradient + greenGradient + redGradient > threshold) 
            { exceedsThreshold = true ; } 
            else 
            { 
                byteOffset -= 2; 

   
                blueGradient = Math.Abs(pixelBuffer[byteOffset - 4] -  
                                        pixelBuffer[byteOffset + 4]); 
                byteOffset++; 

   
                greenGradient = Math.Abs(pixelBuffer[byteOffset - 4] -  
                                         pixelBuffer[byteOffset + 4]); 
                byteOffset++; 

   
                redGradient = Math.Abs(pixelBuffer[byteOffset - 4] -  
                                       pixelBuffer[byteOffset + 4]); 

   
                if (blueGradient + greenGradient + redGradient > threshold) 
                { exceedsThreshold = true ; } 
                else 
                { 
                    byteOffset -= 2; 

   
                    blueGradient =  
                    Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
                    pixelBuffer[byteOffset + sourceData.Stride]); 

   
                    byteOffset++; 

   
                    greenGradient =  
                    Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
                    pixelBuffer[byteOffset + sourceData.Stride]); 

   
                    byteOffset++; 

   
                    redGradient =  
                    Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
                    pixelBuffer[byteOffset + sourceData.Stride]); 

   
                    if (blueGradient + greenGradient +  
                              redGradient > threshold) 
                    { exceedsThreshold = true ; } 
                    else 
                    { 
                        byteOffset -= 2; 

   
                        blueGradient =  
                        Math.Abs(pixelBuffer[byteOffset - 4 - sourceData.Stride] -  
                        pixelBuffer[byteOffset + 4 + sourceData.Stride]); 

   
                        blueGradient +=  
                        Math.Abs(pixelBuffer[byteOffset - sourceData.Stride + 4] -  
                        pixelBuffer[byteOffset + sourceData.Stride - 4]); 

   
                        byteOffset++; 

   
                        greenGradient =  
                        Math.Abs(pixelBuffer[byteOffset - 4 - sourceData.Stride] -  
                        pixelBuffer[byteOffset + 4 + sourceData.Stride]); 

   
                        greenGradient +=  
                        Math.Abs(pixelBuffer[byteOffset - sourceData.Stride + 4] -  
                        pixelBuffer[byteOffset + sourceData.Stride - 4]); 

   
                        byteOffset++; 

   
                        redGradient =  
                        Math.Abs(pixelBuffer[byteOffset - 4 - sourceData.Stride] -  
                        pixelBuffer[byteOffset + 4 + sourceData.Stride]); 

   
                        redGradient += 
                        Math.Abs(pixelBuffer[byteOffset - sourceData.Stride + 4] -  
                        pixelBuffer[byteOffset + sourceData.Stride - 4]); 

   
                        if (blueGradient + greenGradient + redGradient > threshold) 
                        { exceedsThreshold = true ; } 
                        else 
                        { exceedsThreshold = false ; } 
                    } 
                } 
            }

   
            byteOffset -= 2; 

   
            if (exceedsThreshold) 
            { 
                blue = 0; 
                green = 0; 
                red = 0; 
            } 
            else 
            { 
                blue = pixelBuffer[byteOffset]; 
                green = pixelBuffer[byteOffset + 1]; 
                red = pixelBuffer[byteOffset + 2]; 
            } 

   
            blue = (blue > 255 ? 255 : 
                   (blue < 0 ? 0 : 
                    blue)); 

   
            green = (green > 255 ? 255 : 
                    (green < 0 ? 0 : 
                     green)); 

   
            red = (red > 255 ? 255 : 
                  (red < 0 ? 0 : 
                   red));  

   
            resultBuffer[byteOffset] = (byte)blue; 
            resultBuffer[byteOffset + 1] = (byte)green; 
            resultBuffer[byteOffset + 2] = (byte)red; 
            resultBuffer[byteOffset + 3] = 255;   
        } 
    } 

   
    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, 
                                     sourceBitmap.Height); 

   
    BitmapData resultData = 
               resultBitmap.LockBits(new Rectangle(0, 0, 
               resultBitmap.Width, resultBitmap.Height), 
               ImageLockMode.WriteOnly, 
               PixelFormat.Format32bppArgb); 

   
    Marshal.Copy(resultBuffer, 0, resultData.Scan0, 
                               resultBuffer.Length); 

   
    resultBitmap.UnlockBits(resultData); 

   
    return resultBitmap; 
}

Sample Images

The sample image used in this article which features Bill Gates has been licensed under the Creative Commons Attribution 2.0 Generic license and can be downloaded from Wikipedia.

The sample image featuring Steve Ballmer has been licensed under the Creative Commons Attribution 2.0 Generic license and can be downloaded from Wikipedia.

The sample image featuring an Amber flush Rose has been licensed under the Creative Commons Attribution-Share Alike 3.0 Unported, 2.5 Generic, 2.0 Generic and 1.0 Generic license and can be downloaded from Wikipedia.

The sample image featuring a Computer Processor has been licensed under the Creative Commons Attribution-Share Alike 2.0 Generic license and can be downloaded from Wikipedia.l The original author is attributed as Andrew Dunn – http://www.andrewdunnphoto.com/

The Original Image

No Smoothing, Threshold 100

Gaussian 3×3, Threshold 73

Gaussian 5×5, Threshold 78

Gaussian 7×7, Threshold 84

Low Pass 3×3, Threshold 72

Low Pass 5×5, Threshold 81

Mean 3×3, Threshold 79

Mean 5×5, Threshold 80

Median 3×3, Threshold 85

Median 5×5, Threshold 105

Median 7×7, Threshold 127

Median 9×9, Threshold 154

Sharpen 3×3, Threshold 114

C# How to: Gradient Based Edge Detection

Published June 1, 2013 Augmented Reality , C# , Code Samples , Edge Detection , Extension Methods , Graphic Filters , Graphics , How to , Image Convolution , Image Filters , Learn Everyday , Microsoft , Morphology Filters , New Version , Opensource , Update 31 Comments
Tags: Augmented Reality, Binary Image, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, Boolean Edge Detection, C#, Change detection, Code Sample, Computer vision, Converting Images, Convolution filters, Convolution Kernel, Convolution matrix, Edge Masks, Feature extraction, Gradient Edge Detection, Graphic Filters, Image, Image Edge Detection, Image Gradient, Image Morphology, Image processing, Image Sharpen, Image Sharpness, Image Transform, Line Detection, Machine vision, Matrix, Pixel Filters

Article purpose

This article provides a technical discussion exploring the topic of Gradient Based Edge Detection and related aspects. Several filtering options are illustrated and explained ranging from pure black and white edge detection to image sharpening.

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Using the Sample Application

All of the concepts implemented in this article can be replicated and tested by making use of the sample application included in the associated sample source code. The sample application user interface provides several configurable options to be implemented when performing Gradient Based Edge Detection. The available configuration categories are: Filter Type, Derivative Level, Threshold and Colour Factor Filters.

Configurable Filter Types exposed to the end user consist of:

None – When selecting this option no filtering will be applied. Source/input images are displayed reflecting no change.
Edge Detect Mono – This option represents basic Gradient Based Edge Detection. Resulting images are only expressed in terms of black and white pixels.
Edge Detect Gradient – Gradient Based Edge Detection revolves around calculating pixel colour gradients. This option signifies a scenario where the pixels forming resulting images express the relevant pixel’s colour gradient, when a pixel has been determined to reflect part of an edge. If a pixel is not considered to be part of an edge, the relevant pixel’s colour value will be set to black.
Sharpen – In terms of edge detection, image sharpening can be achieved by emphasising detected edges in source/input images. Emphasising edges involves combining a source/input image and an image which express only detected edges.
Sharpen Gradient – This option combines calculated colour gradients and the original colour value of a pixel on a per pixel basis when a pixel has been determined to be part of an edge. If a pixel does not form part of an edge, the pixel’s colour value is set to that of the original pixel colour.

The user interface defines two RadioButtons: First Derivative and Second Derivative. These user interface options relate to the edge detection method being implemented, either First Order or Second Order derivative operators.

Comparing a global threshold and colour gradients on a per pixel scenario forms the basis of Gradient Based Edge Detection. The TrackBar labelled Threshold enables the user to adjust the global threshold value implemented in pixel colour gradient comparisons.

The Colour Factor Filters impact on the level or extent to which colours are expressed in resulting images. The three colour factors, Red, Green and Blue are intended to be used in combination with the filtering options: Filter Type and Threshold. Colour Factor Filter affects when implemented in combination:

Filter Type – Edge Detect Mono: Not applicable. Edge Detect Mono filtering discards all pixel colour data.
Filter Type – Edge Detect Gradient: If a pixel is detected as part of an edge, the pixel’s colour values will be set to the gradient calculated when evaluating edge criteria. Gradient values are multiplied by Colour Factor values before being assigned to a resulting image pixel.
Filter Type – Sharpen: The pixels which form part of an edge, in terms of the resulting image the corresponding pixels will be set to the same colour values. In addition pixel colour values in the resulting image are multiplied by with the relevant Colour Factor. The image pixels not detected as part of an edge will not be multiplied with any Colour Factor values.
Filter Type – Sharpen Gradient: Edge detected pixels in a source/input image will have the effect of corresponding pixels in the resulting image being assigned to the calculated colour gradient value, multiplied with the relevant Colour Factor value. In the scenario of a pixel not being detected as forming part of an edge, Colour Factors will not be implemented.
Threshold: The global threshold value specified by the user determines the level of sensitivity to which edges will be detected. The degree to which edges are detected through the threshold value impacts upon whether a pixel will be multiplied with the relevant Colour Factor value.

The user has the option of saving filtered image to the local file system by clicking the Save Image button. The image below is a screenshot of the Gradient Based Edge Detection sample application in action:

Gradient Based Edge Detection Theory

Gradient Based Edge Detection qualifies to be classified as a neighbouring pixel algorithm. When calculating a pixel’s value in order to determine if a pixel should be expressed as part of an edge or not, the result will be determined by:

The values expressed by neighbouring pixels. The more intense or sudden differences that occur between neighbouring pixels will result in higher accuracy edge detection.
A user specified global threshold value used in comparison operations acts as a cut-off value, ultimately being the final factor to determine if a pixel should be expressed as part of an edge.

In the sample source code we implement the following steps when calculating whether a pixel should be considered as part of an edge:

Iterate each pixel that forms part of the source/input image.
Calculate and combine horizontal and vertical gradients for each of the colour components Red, Green and Blue. If the sum total of each colour component’s calculated gradient exceeds the global threshold value consider the pixel being iterated as part of an edge. If the sum total of colour gradients equate to less than the global threshold implement step 3.
Calculate a pixel’s horizontal gradient per colour component. When comparing the gradient sum total against the global threshold consider the pixel being iterated part of an edge if the sum total of gradient values exceed that of the threshold. If the total of colour gradient value do not exceed the threshold value continue to step 4.
Calculate a pixel’s vertical gradient per colour component. When comparing the gradient sum total against the global threshold consider the pixel being iterated part of an edge if the sum total of gradient values exceed that of the threshold. If the sum total of colour gradients do not exceed the threshold value continue to step 5.
Calculate and combine diagonal gradients for each of the colour components Red, Green and Blue. If the sum total of each colour component’s calculated gradient exceeds the global threshold value consider the pixel being iterated as being part of an edge. If the sum total of colour gradients equate to less than the global threshold the pixel being iterated should not be considered as part of an edge.

If we determined that a pixel forms part of an edge, the value expressed by the corresponding pixel in the resulting image will be determined by the Image Filter configuration value:

Edge Detect Mono – All pixels will be set to white.
Edge Detect Gradient – Each colour component will be assigned to the related colour gradient calculated when performing edge detection. Each Colour gradient will be multiplied with the related colour factor.
Sharpen – The value of a resulting pixel will be calculated as the product of the corresponding source pixel and the related colour factor value.
Sharpen Gradient – Results are calculated in terms of the sum total of the corresponding input pixel and the product of the related colour gradient and colour factor value.

Implementing Gradient Based Edge Detection

The sample source code associated with this article provides the defines of the GradientBasedEdgeDetectionFilter extension method targeting the Bitmap class. This method iterates every pixel contained in the source/input image. Whilst iterating pixels the method creates a 3×3 window/kernel covering the neighbouring pixels surrounding the pixel currently being iterated. Colour gradients are calculated from every pixel’s neighbouring pixels.

The following Code snippet provides the definition of the GradientBasedEdgeDetectionFilter extension method:

public static Bitmap GradientBasedEdgeDetectionFilter( 
                                this Bitmap sourceBitmap, 
                                EdgeFilterType filterType, 
                                DerivativeLevel derivativeLevel,  
                                float redFactor = 1.0f, 
                                float greenFactor = 1.0f, 
                                float blueFactor = 1.0f, 
                                byte threshold = 0) 
{ 
    BitmapData sourceData = 
               sourceBitmap.LockBits(new Rectangle (0, 0, 
               sourceBitmap.Width, sourceBitmap.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 

   
    byte[] pixelBuffer = new byte[sourceData.Stride * 
                                  sourceData.Height]; 

   
    byte[] resultBuffer = new byte[sourceData.Stride * 
                                   sourceData.Height]; 

   
    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, 
                               pixelBuffer.Length); 

   
    sourceBitmap.UnlockBits(sourceData); 

   
    int derivative = (int)derivativeLevel; 
    int byteOffset = 0; 
    int blueGradient, greenGradient, redGradient = 0; 
    double blue = 0, green = 0, red = 0; 

   
    bool exceedsThreshold = false; 

   
    for(int offsetY = 1; offsetY < sourceBitmap.Height - 1; offsetY++) 
    {
        for (int offsetX = 1; offsetX < 
            sourceBitmap.Width - 1; offsetX++) 
        { 
            byteOffset = offsetY * sourceData.Stride + 
                         offsetX * 4; 

   
            blueGradient =  
            Math.Abs(pixelBuffer[byteOffset - 4] - 
            pixelBuffer[byteOffset + 4]) / derivative; 

   
            blueGradient +=  
            Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
            pixelBuffer[byteOffset + sourceData.Stride]) / derivative; 

               
            byteOffset++; 

   
            greenGradient =  
            Math.Abs(pixelBuffer[byteOffset - 4] -  
            pixelBuffer[byteOffset + 4]) / derivative; 

   
            greenGradient +=  
            Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
            pixelBuffer[byteOffset + sourceData.Stride]) / derivative; 

   
            byteOffset++; 

   
            redGradient =  
            Math.Abs(pixelBuffer[byteOffset - 4] -  
            pixelBuffer[byteOffset + 4]) / derivative; 

   
            redGradient +=  
            Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
            pixelBuffer[byteOffset + sourceData.Stride]) / derivative; 

               
            if (blueGradient + greenGradient + redGradient > threshold) 
            { exceedsThreshold = true ; } 
            else 
            { 
                byteOffset -= 2; 

   
                blueGradient = Math.Abs(pixelBuffer[byteOffset - 4] -  
                                        pixelBuffer[byteOffset + 4]); 
                byteOffset++; 

   
                greenGradient = Math.Abs(pixelBuffer[byteOffset - 4] -  
                                         pixelBuffer[byteOffset + 4]); 
                byteOffset++; 

   
                redGradient = Math.Abs(pixelBuffer[byteOffset - 4] -  
                                       pixelBuffer[byteOffset + 4]); 

   
                if (blueGradient + greenGradient + redGradient > threshold) 
                { exceedsThreshold = true ; } 
                else 
                { 
                    byteOffset -= 2; 

   
                    blueGradient =  
                    Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
                    pixelBuffer[byteOffset + sourceData.Stride]); 

   
                    byteOffset++; 

   
                    greenGradient =  
                    Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
                    pixelBuffer[byteOffset + sourceData.Stride]); 

   
                    byteOffset++; 

   
                    redGradient =  
                    Math.Abs(pixelBuffer[byteOffset - sourceData.Stride] -  
                    pixelBuffer[byteOffset + sourceData.Stride]); 

   
                    if (blueGradient + greenGradient +  
                              redGradient > threshold) 
                    { exceedsThreshold = true ; } 
                    else 
                    { 
                        byteOffset -= 2; 

   
                        blueGradient =  
                        Math.Abs(pixelBuffer[byteOffset - 4 - sourceData.Stride] -  
                        pixelBuffer[byteOffset + 4 + sourceData.Stride]) / derivative; 

   
                        blueGradient +=  
                        Math.Abs(pixelBuffer[byteOffset - sourceData.Stride + 4] -  
                        pixelBuffer[byteOffset + sourceData.Stride - 4]) / derivative; 

   
                        byteOffset++; 

   
                        greenGradient =  
                        Math.Abs(pixelBuffer[byteOffset - 4 - sourceData.Stride] -  
                        pixelBuffer[byteOffset + 4 + sourceData.Stride]) / derivative; 

   
                        greenGradient +=  
                        Math.Abs(pixelBuffer[byteOffset - sourceData.Stride + 4] -  
                        pixelBuffer[byteOffset + sourceData.Stride - 4]) / derivative; 

   
                        byteOffset++; 

   
                        redGradient =  
                        Math.Abs(pixelBuffer[byteOffset - 4 - sourceData.Stride] -  
                        pixelBuffer[byteOffset + 4 + sourceData.Stride]) / derivative; 

   
                        redGradient += 
                        Math.Abs(pixelBuffer[byteOffset - sourceData.Stride + 4] -  
                        pixelBuffer[byteOffset + sourceData.Stride - 4]) / derivative; 

   
                        if (blueGradient + greenGradient + redGradient > threshold) 
                        { exceedsThreshold = true ; } 
                        else 
                        { exceedsThreshold = false ; } 
                    } 
                } 
            } 

   
            byteOffset -= 2; 

   
            if (exceedsThreshold) 
            { 
                if (filterType == EdgeFilterType.EdgeDetectMono) 
                { 
                    blue = green = red = 255; 
                } 
                else if (filterType == EdgeFilterType.EdgeDetectGradient) 
                { 
                    blue = blueGradient * blueFactor; 
                    green = greenGradient * greenFactor; 
                    red = redGradient * redFactor; 
                } 
                else if (filterType == EdgeFilterType.Sharpen) 
                {
                    blue = pixelBuffer[byteOffset] * blueFactor; 
                    green = pixelBuffer[byteOffset + 1] * greenFactor; 
                    red = pixelBuffer[byteOffset + 2] * redFactor; 
                } 
                else if (filterType == EdgeFilterType.SharpenGradient) 
                { 
                    blue = pixelBuffer[byteOffset] + blueGradient * blueFactor; 
                    green = pixelBuffer[byteOffset + 1] + greenGradient * greenFactor; 
                    red = pixelBuffer[byteOffset + 2] + redGradient * redFactor; 
                } 
            } 
            else 
            { 
                if (filterType == EdgeFilterType.EdgeDetectMono ||  
                    filterType == EdgeFilterType.EdgeDetectGradient) 
                { 
                    blue = green = red = 0; 
                } 
                else if (filterType == EdgeFilterType.Sharpen ||  
                         filterType == EdgeFilterType.SharpenGradient) 
                { 
                    blue = pixelBuffer[byteOffset]; 
                    green = pixelBuffer[byteOffset + 1]; 
                    red = pixelBuffer[byteOffset + 2]; 
                } 
            } 

   
            blue = (blue > 255 ? 255 : 
                   (blue < 0 ? 0 : 
                    blue)); 

   
            green = (green > 255 ? 255 : 
                    (green < 0 ? 0 : 
                     green)); 

   
            red = (red > 255 ? 255 : 
                  (red < 0 ? 0 : 
                   red));  

   
            resultBuffer[byteOffset] = (byte)blue; 
            resultBuffer[byteOffset + 1] = (byte)green; 
            resultBuffer[byteOffset + 2] = (byte)red; 
            resultBuffer[byteOffset + 3] = 255;   
        } 
    } 
 
    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, 
                                     sourceBitmap.Height); 

   
    BitmapData resultData = 
               resultBitmap.LockBits(new Rectangle (0, 0, 
               resultBitmap.Width, resultBitmap.Height), 
               ImageLockMode.WriteOnly, 
               PixelFormat.Format32bppArgb); 

   
    Marshal.Copy(resultBuffer, 0, resultData.Scan0, 
                               resultBuffer.Length); 

   
    resultBitmap.UnlockBits(resultData); 

   
    return resultBitmap; 
}

Sample Images

The banner images depicting a butterfly featured throughout this article were generated using the sample application. The original image has been licenced under the Creative Commons Attribution-Share Alike 3.0 Unported, 2.5 Generic, 2.0 Generic and 1.0 Generic license. The original image is attributed to Kenneth Dwain Harrelson and can be downloaded from Wikipedia.

The sample image featuring a Scarlet Macaw has been licensed under the Creative Commons Attribution-Share Alike 3.0 Germany license. The original image can be downloaded from Wikipedia.

The Original Image

Edge Detect, Second Derivative, Threshold 50

Edge Detect Gradient, First Derivative, Blue

Edge Detect Gradient, First Derivative, Green

Edge Detect Gradient, First Derivative, Green and Blue

Edge Detect Gradient, First Derivative, Red

Edge Detect Gradient, First Derivative, Red and Blue

Edge Detect Gradient, First Derivative, Red and Green

Edge Detect Gradient, First Derivative, Red, Green and Blue

Edge Detect Sharpen, Second Derivative, Threshold 40, Black

Edge Detect Sharpen, Second Derivative, Threshold 40, Blue

Edge Detect Sharpen, Second Derivative, Threshold 40, Green

Edge Detect Sharpen, Second Derivative, Threshold 40, Green and Blue

Edge Detect Sharpen, Second Derivative, Threshold 40, Red

Edge Detect Sharpen, Second Derivative, Threshold 40, Red and Blue

Edge Detect Sharpen, Second Derivative, Threshold 40, Red and Green

Edge Detect Sharpen, Second Derivative, Threshold 40, White

Edge Detect Sharpen Gradient, First Derivative, Threshold 0, Blue

Edge Detect Sharpen Gradient, First Derivative, Threshold 0, Green

Edge Detect Sharpen Gradient, First Derivative, Threshold 0, Green and Blue

Edge Detect Sharpen Gradient, First Derivative, Threshold 0, Red

Edge Detect Sharpen Gradient, First Derivative, Threshold 0, Red and Blue

Edge Detect Sharpen Gradient, First Derivative, Threshold 0, Red and Green

Edge Detect Sharpen Gradient, First Derivative, Threshold 0, White

C# How to: Image Unsharp Mask

Published May 18, 2013 Augmented Reality , C# , Code Samples , Extension Methods , Graphic Filters , Graphics , How to , Image Convolution , Image Filters , Microsoft , Opensource , Tip 32 Comments
Tags: Augmented Reality, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, BitmapData.Stride, C#, C# Convolution, Code Sample, Computer vision, Convolution filters, Convolution Kernel, Convolution matrix, Edge enhance, Extension Methods, Feature extraction, Gaussian, Gaussian Blur, Graphic Filters, Image Blur, Image processing, Image Sharpness, Image Transform, Machine vision, Matrix, Mean Filter, Pixel Filters, Pixel Manipulation, Unsharp, Unsharp Masking

Article purpose

The purpose of this article is to explore and illustrate the concept of Image Unsharp Masking. This article implements image convolution in the form of a 3×3 Gaussian Blur, 5×5 Gaussian Blur, 3×3 Mean filter and a 5×5 Mean filter.

Sample Source code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Using the Sample Application

The sample source code associated with this article includes a Windows Forms based sample application implementing the concepts explored throughout this article.

When using the Image Unsharp Mask sample application users can select a source/input image from the local file system by clicking the Load Image button. The dropdown combobox at the bottom of the screen allows the user to select an unsharp masking variation. On the right hand side of the screen users can specify the level/intensity of resulting image sharpness.

Clicking the Save Image button allows a user to save resulting images to the local file system. The image below is a screenshot of the Image Unsharp Mask sample application in action:

What is Image Unsharp Masking?

A good definition of Image Unsharp Masking can be found on Wikipedia:

Unsharp masking (USM) is an image manipulation technique, often available in digital image processing software.

The "unsharp" of the name derives from the fact that the technique uses a blurred, or "unsharp", positive image to create a "mask" of the original image. The unsharped mask is then combined with the negative image, creating an image that is less blurry than the original. The resulting image, although clearer, probably loses accuracy with respect to the image’s subject. In the context of signal-processing, an unsharp mask is generally a linear or nonlinear filter that amplifies high-frequency components.

In this article we implement Image Unsharp Masking by first creating a blurred copy of a source/input image then subtracting the blurred image from the original image, which is known as the mask. Increased image sharpness is achieved by adding a factor of the mask to the original image.

Applying a Convolution Matrix filter

The sample source code provides the definition for the ConvolutionFilter extension method targeting the Bitmap class. This method is invoked when implementing image blurring. The definition of the ConvolutionFilter extension method as follows:

 private static Bitmap ConvolutionFilter(Bitmap sourceBitmap,  
                                     double[,] filterMatrix,  
                                          double factor = 1,  
                                               int bias = 0,  
                                     bool grayscale = false )  
{ 
     BitmapData sourceData = sourceBitmap.LockBits(new Rectangle (0, 0, 
                              sourceBitmap.Width, sourceBitmap.Height), 
                                                ImageLockMode.ReadOnly,  
                                          PixelFormat.Format32bppArgb); 

  
     byte[] pixelBuffer = new byte[sourceData.Stride * sourceData.Height]; 
     byte[] resultBuffer = new byte[sourceData.Stride * sourceData.Height]; 

  
     Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, pixelBuffer.Length); 
     sourceBitmap.UnlockBits(sourceData); 

  
     if (grayscale == true) 
     {
         float rgb = 0; 

  
         for (int k = 0; k < pixelBuffer.Length; k += 4) 
         { 
             rgb = pixelBuffer[k] * 0.11f; 
             rgb += pixelBuffer[k + 1] * 0.59f; 
             rgb += pixelBuffer[k + 2] * 0.3f; 

  
             pixelBuffer[k] = (byte )rgb; 
             pixelBuffer[k + 1] = pixelBuffer[k]; 
             pixelBuffer[k + 2] = pixelBuffer[k]; 
             pixelBuffer[k + 3] = 255; 
         } 
     } 

  
     double blue = 0.0; 
     double green = 0.0; 
     double red = 0.0; 

  
     int filterWidth = filterMatrix.GetLength(1); 
     int filterHeight = filterMatrix.GetLength(0); 

  
     int filterOffset = (filterWidth-1) / 2; 
     int calcOffset = 0; 

  
     int byteOffset = 0; 

  
     for (int offsetY = filterOffset; offsetY <  
         sourceBitmap.Height - filterOffset; offsetY++) 
     { 
         for (int  offsetX = filterOffset; offsetX <  
             sourceBitmap.Width - filterOffset; offsetX++) 
         { 
             blue = 0; 
             green = 0; 
             red = 0; 

  
             byteOffset = offsetY *  
                          sourceData.Stride +  
                          offsetX * 4; 

   
             for (int  filterY = -filterOffset;  
                 filterY <= filterOffset; filterY++) 
             {
                 for (int  filterX = -filterOffset; 
                     filterX <= filterOffset; filterX++) 
                 { 

   
                     calcOffset = byteOffset +  
                                  (filterX * 4) +  
                                  (filterY * sourceData.Stride); 

  
                     blue += (double)(pixelBuffer[calcOffset]) * 
                             filterMatrix[filterY + filterOffset,  
                                          filterX + filterOffset]; 

  
                     green += (double)(pixelBuffer[calcOffset + 1]) * 
                              filterMatrix[filterY + filterOffset,  
                                           filterX + filterOffset]; 

   
                     red += (double)(pixelBuffer[calcOffset + 2]) * 
                            filterMatrix[filterY + filterOffset,  
                                         filterX + filterOffset]; 
                 }
             }

   
             blue = factor * blue + bias; 
             green = factor * green + bias; 
             red = factor * red + bias; 

  
             if (blue > 255) 
             { blue = 255; } 
             else if (blue < 0) 
             { blue = 0; } 

  
             if (green > 255) 
             { green = 255; } 
             else if (green < 0) 
             { green = 0; } 

  
             if (red > 255) 
             { red = 255; } 
             else if (red < 0) 
             { red = 0; } 

  
             resultBuffer[byteOffset] = (byte )(blue); 
             resultBuffer[byteOffset + 1] = (byte )(green); 
             resultBuffer[byteOffset + 2] = (byte )(red); 
             resultBuffer[byteOffset + 3] = 255; 
        }
    } 

  
     Bitmap resultBitmap = new Bitmap(sourceBitmap.Width,
                                      sourceBitmap.Height); 
  
     BitmapData resultData = resultBitmap.LockBits(new Rectangle (0, 0, 
                              resultBitmap.Width, resultBitmap.Height), 
                                               ImageLockMode.WriteOnly, 
                                          PixelFormat.Format32bppArgb); 

  
     Marshal.Copy(resultBuffer, 0, resultData.Scan0, resultBuffer.Length); 
     resultBitmap.UnlockBits(resultData); 

   
     return resultBitmap; 
}

Subtracting and Adding Images

An important step required when implementing Image Unsharp Masking comes in the form of creating a mask by subtracting a blurred copy from the original image and then adding a factor of the mask to the original image. In order to achieve increased performance the sample source code combines the process of creating the mask and adding the mask to the original image.

The SubtractAddFactorImage extension method iterates every pixel that forms part of an image. In a single step the blurred pixel is subtracted from the original pixel, multiplied by a user specified factor and then added to the original pixel. The definition of the SubtractAddFactorImage extension method as follows:

private static Bitmap SubtractAddFactorImage( 
                              this Bitmap subtractFrom, 
                                  Bitmap subtractValue, 
                                   float factor = 1.0f) 
{ 
    BitmapData sourceData =  
               subtractFrom.LockBits(new Rectangle (0, 0, 
               subtractFrom.Width, subtractFrom.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 

  
    byte[] sourceBuffer = new byte[sourceData.Stride *  
                                   sourceData.Height]; 

  
    Marshal.Copy(sourceData.Scan0, sourceBuffer, 0,  
                                sourceBuffer.Length); 

  
    byte[] resultBuffer = new byte[sourceData.Stride *  
                                   sourceData.Height]; 

  
    BitmapData subtractData =  
               subtractValue.LockBits(new Rectangle (0, 0, 
               subtractValue.Width, subtractValue.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 

  
    byte[] subtractBuffer = new byte[subtractData.Stride * 
                                     subtractData.Height]; 

  
    Marshal.Copy(subtractData.Scan0, subtractBuffer, 0, 
                                 subtractBuffer.Length); 

  
    subtractFrom.UnlockBits(sourceData); 
    subtractValue.UnlockBits(subtractData); 

  
    double blue = 0; 
    double green = 0; 
    double red = 0; 

  
    for (int k = 0; k < resultBuffer.Length && 
                   k < subtractBuffer.Length; k += 4) 
    { 
        blue = sourceBuffer[k] +  
              (sourceBuffer[k] - 
               subtractBuffer[k]) * factor; 

  
        green = sourceBuffer[k + 1] +  
               (sourceBuffer[k + 1] - 
                subtractBuffer[k + 1]) * factor; 

  
        red = sourceBuffer[k + 2] +  
             (sourceBuffer[k + 2] - 
              subtractBuffer[k + 2]) * factor; 

  
        blue = (blue < 0 ? 0 : (blue > 255 ? 255 : blue)); 
        green = (green < 0 ? 0 : (green > 255 ? 255 : green)); 
        red = (red < 0 ? 0 : (red > 255 ? 255 : red)); 

  
        resultBuffer[k] = (byte )blue; 
        resultBuffer[k + 1] = (byte )green; 
        resultBuffer[k + 2] = (byte )red; 
        resultBuffer[k + 3] = 255; 
    }

  
    Bitmap resultBitmap = new Bitmap (subtractFrom.Width,  
                                     subtractFrom.Height); 

  
    BitmapData resultData =  
               resultBitmap.LockBits(new Rectangle (0, 0, 
               resultBitmap.Width, resultBitmap.Height), 
               ImageLockMode.WriteOnly, 
               PixelFormat.Format32bppArgb); 

  
    Marshal.Copy(resultBuffer, 0, resultData.Scan0,  
                               resultBuffer.Length); 

  
    resultBitmap.UnlockBits(resultData); 

  
    return resultBitmap; 
}

Matrix Definition

The image blurring convolution filters implemented by the sample source code relies on static matrix/kernel values defined in the Matrix class. The variants of blurring implemented are: 3×3 Gaussian, 5×5 Gaussian, 3×3 Mean and 5×5 Mean. The definition of the Matrix class is detailed by the following code snippet:

public static class Matrix
{
    public static double[,] Gaussian3x3
    {
        get
        {
            return new double[,]
            { { 1, 2, 1, }, 
              { 2, 4, 2, }, 
              { 1, 2, 1, }, };
        }
    }

  
    public static double[,] Gaussian5x5Type1
    {
        get
        {
            return new double[,]
            { { 2, 04, 05, 04, 2 }, 
              { 4, 09, 12, 09, 4 },
              { 5, 12, 15, 12, 5 },
              { 4, 09, 12, 09, 4 },
              { 2, 04, 05, 04, 2 }, };
        }
    }

  
    public static double[,] Mean3x3
    {
        get
        {
            return new double[,]
            { { 1, 1, 1, }, 
              { 1, 1, 1, }, 
              { 1, 1, 1, }, };
        }
    }

  
    public static double[,] Mean5x5
    {
        get
        {
            return new double[,]
            { { 1, 1, 1, 1, 1 }, 
              { 1, 1, 1, 1, 1 },
              { 1, 1, 1, 1, 1 },
              { 1, 1, 1, 1, 1 },
              { 1, 1, 1, 1, 1 }, };
        }
    }
}

Implementing Image Unsharpening

This article explores four variants of Image Unsharp Masking, relating to the four types of image blurring discussed in the previous section. The sample source code defines the following Image Unsharp Masking extension methods: UnsharpGaussian3x3, UnsharpGaussian5x5, UnsharpMean3x3 and UnsharpMean5x5. All four methods are defined as extension methods targeting the Bitmap class. When looking at the sample images in the following section you will notice the correlation between increased image blurring and enhanced image sharpness. The definition as follows:

public static Bitmap UnsharpGaussian3x3( 
                                 this Bitmap sourceBitmap,  
                                 float factor = 1.0f) 
{
    Bitmap blurBitmap = ExtBitmap.ConvolutionFilter( 
                                  sourceBitmap,  
                                  Matrix.Gaussian3x3,  
                                  1.0 / 16.0); 

   
    Bitmap resultBitmap =  
           sourceBitmap.SubtractAddFactorImage( 
                        blurBitmap, factor); 

   
    return resultBitmap; 
 } 

   
public static Bitmap UnsharpGaussian5x5( 
                                 this Bitmap sourceBitmap, 
                                 float factor = 1.0f) 
{
    Bitmap blurBitmap = ExtBitmap.ConvolutionFilter( 
                                  sourceBitmap,  
                                  Matrix.Gaussian5x5Type1,  
                                  1.0 / 159.0); 

   
    Bitmap resultBitmap = 
           sourceBitmap.SubtractAddFactorImage( 
                        blurBitmap, factor); 

   
    return resultBitmap; 
} 
 
public static Bitmap UnsharpMean3x3( 
                                 this Bitmap sourceBitmap, 
                                 float factor = 1.0f) 
{ 
    Bitmap blurBitmap = ExtBitmap.ConvolutionFilter( 
                                  sourceBitmap,  
                                  Matrix.Mean3x3,  
                                  1.0 / 9.0); 

   
    Bitmap resultBitmap = 
           sourceBitmap.SubtractAddFactorImage( 
                        blurBitmap, factor); 

   
    return resultBitmap; 
}

   
public static Bitmap UnsharpMean5x5( 
                                 this Bitmap sourceBitmap, 
                                 float factor = 1.0f) 
{ 
    Bitmap blurBitmap = ExtBitmap.ConvolutionFilter( 
                                  sourceBitmap,  
                                  Matrix.Mean5x5,  
                                  1.0 / 25.0); 

   
    Bitmap resultBitmap = 
           sourceBitmap.SubtractAddFactorImage( 
                        blurBitmap, factor); 

   
    return resultBitmap; 
}

Sample Images

The input/source image used in rendering the sample images shown in this article is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license and can be downloaded from Wikipedia:

The Original Image

Unsharp Gaussian 3×3

Unsharp Gaussian 5×5

Unsharp Mean 3×3

Unsharp Gaussian 5×5

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Dewald Esterhuizen

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