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C# How to: Fuzzy Blur Filter

Article Purpose

This article serves to illustrate the concepts involved in implementing a Fuzzy Blur Filter. This filter results in rendering non-photo realistic images which express a certain artistic effect.

Frog: Filter Size 19×19

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 test application. The concepts explored throughout this article can be replicated/tested using the sample application.

When executing the sample application the user interface exposes a number of configurable options:

Loading and Saving Images – Users are able to load source/input images from the local file system by clicking the Load Image button. Clicking the Save Image button allow users to save filter result images.
Filter Size – The specified filter size affects the filter intensity. Smaller filter sizes result in less blurry images being rendered, whereas larger filter sizes result in more blurry images being rendered.
Edge Factors – The contrast of fuzzy image noise expressed in resulting images depend on the specified edge factor values. Values less than one result in detected image edges being darkened and values greater than one result in detected image edges being lightened.

The following image is a screenshot of the Fuzzy Blur Filter sample application in action:

Frog: Filter Size 9×9

Fuzzy Blur Overview

The Fuzzy Blur Filter relies on the interference of image noise when performing edge detection in order to create a fuzzy effect. In addition image blurring results from performing a mean filter.

The steps involved in performing a Fuzzy Blur Filter can be described as follows:

Edge Detection and Enhancement – Using the first edge factor specified enhance image edges by performing Boolean Edge detection. Being sensitive to image noise, a fair amount of detected image edges will actually be image noise in addition to actual image edges.
Mean Filter Blur – Using the edge enhanced image created in the previous step perform a mean filter blur. The enhanced edges will be blurred since a Mean filter does not have edge preservation properties. The size of the Mean filter implemented depends on a user specified value.
Edge Detection and Enhancement – Using the Mean filter blurred image created in the previous step once again perform Boolean Edge detection, enhancing detected edges according to the second edge factor specified.

Frog: Filter Size 9×9

Mean Filter Blurring

A Mean Filter Blur, also known as a Box Blur, can be performed through image convolution. The size of the matrix/kernel implemented when preforming image convolution will be determined through user input.

Every matrix/kernel element should be set to one. The resulting value should be multiplied by a factor value equating to one divided by the matrix/kernel size. As an example, a matrix/kernel size of 3×3 can be expressed as follows:

An alternative expression can also be:

Frog: Filter Size 9×9

Boolean Edge Detection without a local threshold

When performing Boolean Edge Detection a local threshold should be implemented in order to exclude image noise. In this article we rely on the interference of image noise in order to render a fuzzy image effect. By not implementing a local threshold when performing Boolean Edge detection the sample source code ensures sufficient interference from image noise.

The steps involved in performing Boolean Edge Detection without a local threshold can be described as follows:

Calculate Neighbourhood Mean – Iterate each pixel forming part of the source/input image. Using a 3×3 matrix size calculate the mean value of the neighbourhood surrounding the pixel currently being iterated.
Create Mean comparison Matrix – Once again using a 3×3 matrix size compare each neighbourhood pixel to the newly calculated mean value. Create a temporary 3×3 size matrix, each matrix element’s value should be the result of mean comparison. Should the value expressed by a neighbourhood pixel exceed the mean value the corresponding temporary matrix element should be set to one. When the calculated mean value exceeds the value of a neighbourhood pixel the corresponding temporary matrix element should be set to zero.
Compare Edge Masks – Using sixteen predefined edge masks compare the temporary matrix created in the previous step to each edge mask. If the temporary matrix matches one of the predefined edge masks multiply the specified factor to the pixel currently being iterated.

Note: A detailed article on Boolean Edge detection implementing a local threshold can be found here: C# How to: Boolean Edge Detection

Frog: Filter Size 9×9

The sixteen predefined edge masks each represent an image edge in a different direction. The predefined edge masks can be expressed as:

Frog: Filter Size 13×13

Implementing a Mean Filter

The sample source code defines the MeanFilter method, an extension method targeting the Bitmap class. The definition listed as follows:

private static Bitmap MeanFilter(this Bitmap sourceBitmap, 
                                 int meanSize)
{
    byte[] pixelBuffer = sourceBitmap.GetByteArray(); 
    byte[] resultBuffer = new byte[pixelBuffer.Length];


    double blue = 0.0, green = 0.0, red = 0.0;
    double factor = 1.0 / (meanSize * meanSize);


    int imageStride = sourceBitmap.Width * 4;
    int filterOffset = meanSize / 2; 
    int calcOffset = 0, filterY = 0, filterX = 0; 


    for (int k = 0; k + 4 < pixelBuffer.Length; k += 4)
    {
        blue = 0; green = 0; red = 0;
        filterY = -filterOffset;
        filterX = -filterOffset;


        while (filterY <= filterOffset)
        {
            calcOffset = k + (filterX * 4) +
            (filterY * imageStride); 


            calcOffset = (calcOffset < 0 ? 0 :
            (calcOffset >= pixelBuffer.Length - 2 ?
            pixelBuffer.Length - 3 : calcOffset));


            blue += pixelBuffer[calcOffset];
            green += pixelBuffer[calcOffset + 1];
            red += pixelBuffer[calcOffset + 2];


            filterX++;


            if (filterX > filterOffset)
            {
                filterX = -filterOffset;
                filterY++;
            }
        }


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


    return resultBuffer.GetImage(sourceBitmap.Width, sourceBitmap.Height);
}

Frog: Filter Size 19×19

Implementing Boolean Edge Detection

Boolean Edge detection is performed in the sample source code through the implementation of the BooleanEdgeDetectionFilter method. This method has been defined as an extension method targeting the Bitmap class.

The following code snippet provides the definition of the BooleanEdgeDetectionFilter extension method:

public static Bitmap BooleanEdgeDetectionFilter( 
       this Bitmap sourceBitmap, float edgeFactor) 
{
    byte[] pixelBuffer = sourceBitmap.GetByteArray(); 
    byte[] resultBuffer = new byte[pixelBuffer.Length]; 
    Buffer.BlockCopy(pixelBuffer, 0, resultBuffer, 
                     0, pixelBuffer.Length); 


    List<string> edgeMasks = GetBooleanEdgeMasks(); 
 
    int imageStride = sourceBitmap.Width * 4; 
    int matrixMean = 0, pixelTotal = 0; 
    int filterY = 0, filterX = 0, calcOffset = 0; 
    string matrixPatern = String.Empty; 


    for (int k = 0; k + 4 < pixelBuffer.Length; k += 4) 
    {
        matrixPatern = String.Empty; 
        matrixMean = 0; pixelTotal = 0; 
        filterY = -1; filterX = -1; 


        while (filterY < 2) 
        {
            calcOffset = k + (filterX * 4) + 
            (filterY * imageStride); 


            calcOffset = (calcOffset < 0 ? 0 : 
            (calcOffset >= pixelBuffer.Length - 2 ? 
            pixelBuffer.Length - 3 : calcOffset)); 
 
            matrixMean += pixelBuffer[calcOffset]; 
            matrixMean += pixelBuffer[calcOffset + 1]; 
            matrixMean += pixelBuffer[calcOffset + 2]; 


            filterX += 1; 


            if (filterX > 1) 
             { filterX = -1; filterY += 1; } 
        }


        matrixMean = matrixMean / 9; 
        filterY = -1; filterX = -1; 


        while (filterY < 2) 
        {
            calcOffset = k + (filterX * 4) + 
            (filterY * imageStride); 


            calcOffset = (calcOffset < 0 ? 0 : 
            (calcOffset >= pixelBuffer.Length - 2 ? 
            pixelBuffer.Length - 3 : calcOffset)); 


            pixelTotal = pixelBuffer[calcOffset]; 
            pixelTotal += pixelBuffer[calcOffset + 1]; 
            pixelTotal += pixelBuffer[calcOffset + 2]; 
 
            matrixPatern += (pixelTotal > matrixMean 
                                       ? "1" : "0"); 
            filterX += 1; 


            if (filterX > 1) 
            { filterX = -1; filterY += 1; } 
        } 


        if (edgeMasks.Contains(matrixPatern)) 
        {
            resultBuffer[k] = 
            ClipByte(resultBuffer[k] * edgeFactor); 


            resultBuffer[k + 1] = 
            ClipByte(resultBuffer[k + 1] * edgeFactor); 


            resultBuffer[k + 2] = 
            ClipByte(resultBuffer[k + 2] * edgeFactor); 
        }
    }


    return resultBuffer.GetImage(sourceBitmap.Width, sourceBitmap.Height); 
}

Frog: Filter Size 13×13

The predefined edge masks implemented in mean comparison have been wrapped by the GetBooleanEdgeMasks method. The definition as follows:

public static List<string> GetBooleanEdgeMasks() 
{
    List<string> edgeMasks = new List<string>(); 


    edgeMasks.Add("011011011"); 
    edgeMasks.Add("000111111"); 
    edgeMasks.Add("110110110"); 
    edgeMasks.Add("111111000"); 
    edgeMasks.Add("011011001"); 
    edgeMasks.Add("100110110"); 
    edgeMasks.Add("111011000"); 
    edgeMasks.Add("111110000"); 
    edgeMasks.Add("111011001"); 
    edgeMasks.Add("100110111"); 
    edgeMasks.Add("001011111"); 
    edgeMasks.Add("111110100"); 
    edgeMasks.Add("000011111"); 
    edgeMasks.Add("000110111"); 
    edgeMasks.Add("001011011"); 
    edgeMasks.Add("110110100"); 


    return edgeMasks; 
}

Frog: Filter Size 19×19

Implementing a Fuzzy Blur Filter

The FuzzyEdgeBlurFilter method serves as the implementation of a Fuzzy Blur Filter. As discussed earlier a Fuzzy Blur Filter involves enhancing image edges through Boolean Edge detection, performing a Mean Filter blur and then once again performing Boolean Edge detection. This method has been defined as an extension method targeting the Bitmap class.

The following code snippet provides the definition of the FuzzyEdgeBlurFilter method:

public static Bitmap FuzzyEdgeBlurFilter(this Bitmap sourceBitmap,  
                                         int filterSize,  
                                         float edgeFactor1,  
                                         float edgeFactor2) 
{
    return  
    sourceBitmap.BooleanEdgeDetectionFilter(edgeFactor1). 
    MeanFilter(filterSize).BooleanEdgeDetectionFilter(edgeFactor2); 
}

Frog: Filter Size 3×3

Sample Images

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

Tyler’s Tree Frog
Attribution: LiquidGhoul. This file has been released into the public domain by its author, LiquidGhoul. This applies worldwide.
Download from Wikipedia.

Phyllobates Terribilis
Attribution: Wilfried Berns. This file is licensed under the Creative Commons Attribution-Share Alike 2.0 Germany license.
Download from Wikipedia.

Dendropsophus Microcephalus
Attribution: Brian Gratwicke. This file is licensed under the Creative Commons Attribution 2.0 Generic license.
Download from Wikipedia

Panamanian Golden Frog
Attribution: Brian Gratwicke. This file is licensed under the Creative Commons Attribution 2.0 Generic license.
Download from Wikipedia.

C# How to: Oil Painting and Cartoon Filter

Published June 29, 2013 Augmented Reality , C# , Code Samples , Edge Detection , Extension Methods , Graphic Filters , Graphics , How to , Image Arithmetic , Image Filters , Image Processing , Learn Everyday , Microsoft , New Version , Opensource , Tip , Update 29 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

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: Image Blur

Published June 9, 2013 Augmented Reality , C# , Code Samples , Extension Methods , Graphic Filters , Graphics , How to , Image Convolution , Image Filters , Image Processing , Learn Everyday , Microsoft , New Version , Opensource , Update 29 Comments
Tags: Augmented Reality, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, Box Blur, C#, Calculating Kernels, Code Sample, Converting Images, Convolution matrix, Feature extraction, Gaussian, Gaussian Blur, Gaussian Kernels, Graphic Filters, Image, Image Blur, Image processing, Image Smoothing, Image Transform, Machine vision, Matrix, Mean Filter, Median Filter, Motion Blur, Pixel Filters

Article Purpose

This article serves to provides an introduction and discussion relating to Image Blurring methods and techniques. The Image Blur methods covered in this article include: Box Blur, Gaussian Blur, Mean Filter, Median Filter and Motion Blur.

Daisy: Mean 9×9

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

This article is accompanied by a sample application, intended to provide a means of testing and replicating topics discussed in this article. The sample application is a Windows Forms based application of which the user interface enables the user to select an Image Blur type to implement.

When clicking the Load Image button users are able to browse the local file system in order to select source/input images. In addition users are also able to save blurred result images when clicking the Save Image button and browsing the local file system.

Daisy: Mean 7×7

The sample application provides the user with the ability to select the method of image blurring to implement. The combobox dropdown located on the right-hand side of the user interface lists all of the supported methods of image blurring. When a user selects an item from the combobox, the associated blur method will be implemented on the preview image.

The image below is a screenshot of the Image Blur Filter sample application in action:

Image Blur Overview

The process of image blurring can be regarded as reducing the sharpness or crispness defined by an image. Image blurring results in image detail/definition being perceived as less distinct. Images are often blurred as a method of smoothing an image.

Images perceived as too crisp/sharp can be softened by applying a variety of image blurring techniques and intensity levels. Often images are smoothed/blurred in order to remove/reduce image noise. In image edge detection implementations better results are often achieved when first implementing noise reduction through smoothing/blurring. Image blurring can even be implemented in a fashion where results reflect image edge detection, a method known as Difference of Gaussians.

In this article and the accompanying sample source code all methods of image blurring supported have been implemented through image convolution, with the exception of the Median filter. Each of the supported methods in essence only represent a different convolution matrix kernel. The image blurring technique capable of achieving optimal results will to varying degrees be dependent on the features present in the specified source/input image. Each method provides a different set of desired properties and compromises. In the following sections an overview of each method will be discussed.

Daisy: Mean 9×9

Mean Filter/Box Blur

The Mean Filter also sometimes referred to as a Box Blur represents a fairly simplistic implementation and definition. A Mean Filter definition can be found on Wikipedia as follows:

A box blur is an image filter in which each pixel in the resulting image has a value equal to the average value of its neighbouring pixels in the input image. It is a form of low-pass ("blurring") filter and is a convolution.

Due to its property of using equal weights it can be implemented using a much simpler accumulation algorithm which is significantly faster than using a sliding window algorithm.

Mean Filter as a title relates to all weight values in a convolution kernel being equal, therefore the alternate title of Box Blur. In most cases a Mean Filter matrix kernel will only contain the value one. When performing image convolution implementing a Mean Filter kernel, the factor value equates to the 1 being divided by the sum of all kernel values.

The following is an example of a 5×5 Mean Filter convolution kernel:

The kernel consist of 25 elements, therefore the factor value equates to one divided by twenty five.

The Mean Filter Blur does not result in the same level of smoothing achieved by other image blur methods. The Mean Filter method can also be susceptible to directional artefacts.

Daisy Mean 5×5

Gaussian Blur

The Gaussian method of image blurring is a popular and often implemented filter. In contrast to the Box Blur method Gaussian Blurring produce resulting images appearing to contain a more uniform level of smoothing. When implementing image edge detection a Gaussian Blur is often applied to source/input images resulting in noise reduction. The Gaussian Blur has a good level of image edge preservation, hence being used in edge detection operations.

From Wikipedia we gain the following description:

A Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function. It is a widely used effect in graphics software, typically to reduce image noise and reduce detail. The visual effect of this blurring technique is a smooth blur resembling that of viewing the image through a translucent screen, distinctly different from the bokeh effect produced by an out-of-focus lens or the shadow of an object under usual illumination. Gaussian smoothing is also used as a pre-processing stage in computer vision algorithms in order to enhance image structures at different scales

A potential drawback to implementing a Gaussian blur results from the filter being computationally intensive. The following matrix kernel represents a 5×5 Gaussian Blur. The sum total of all elements in the kernel equate to 159, therefore a factor value of 1.0 / 159.0 will be implemented.

Daisy: Gaussian 5×5

Median Filter Blur

The Median Filter is classified as a non-linear filter. In contrast to the other methods of image blurring discussed in this article the Median Filter implementation does not involve convolution or a predefined matrix kernel. The following description can be found on Wikipedia:

In signal processing, it is often desirable to be able to perform some kind of noise reduction on an image or signal. The median filter is a nonlinear digital filtering technique, often used to remove noise. Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image). Median filtering is very widely used in digital image processing because, under certain conditions, it preserves edges while removing noise.

Daisy: Median 7×7

As the name implies, the Median Filter operates by calculating the median value of a pixel group also referred to as a window. Calculating a Median value involves a number of steps. The required steps are listed as follows:

Iterate each pixel that forms part of the source/input image.
In relation to the pixel currently being iterated determine neighbouring pixels located within the bounds defined by the window size. The window location should be offset in order to align the window’s middle pixel and the pixel currently being iterated.
Neighbouring pixels located within the bounds defined by the window should be added to a one dimensional neighbourhood array. Once all value have been added, the array should be sorted by value.
The pixel value located at the middle of the sorted neighbourhood array qualifies as the Median value. The newly determined Median value should be assigned to the pixel currently being iterated.
Repeat the steps listed above until all pixels within the source/input image have been iterated.

Similar to the Gaussian Blur filter the Median Filter has the ability to smooth image noise whilst providing edge preservation. Depending on the window size implemented and the physical dimensions of input/source images the Median Filter can be computationally expensive.

Daisy: Median 9×9

Motion Blur

The sample source implements Motion Blur filters. Motion blurring in the traditional sense has been association with photography and video capturing. Motion Blurring can often be observed in scenarios where rapid movements are being captured to photographs or video recording. When recording a single frame, rapid movements could result in the image changing before the frame being captured has completed.

Motion Blurring can be synthetically imitated through the implementation of Digital Motion Blur filters. The size of the matrix kernel provided when implementing image convolution affects the filter intensity perceived in result images. Relating to Motion Blur filters the size of the kernel specified in convolution influences the perception and appearance of how rapidly movement had occurred to have blurred the resulting image. Larger kernels produce the appearance of more rapid motion, whereas smaller kernels result in less rapid motion being perceived.

Daisy: Motion Blur 7×7 135 Degrees

Depending on the kernel specified the ability exists to create the appearance of movement having occurred in a certain direction. The sample source code implements Motion Blur filters at 45 degrees, 135 degrees and in both directions simultaneously.

The kernel listed below represents a 5×5 Motion Blur filter occurring at 45 degrees and 135 degrees:

Image Blur Implementation

The sample source code implements all of the concepts explored throughout this article. The source code definition can be grouped into 4 sections: ImageBlurFilter method, ConvolutionFilter method, MedianFilter method and the Matrix class. The following article sections relate to the 4 main source code sections.

The ImageBlurFilter extension method has the purpose of invoking the correct blur filter method and relevant method parameters. This method acts as a method wrapper providing the technical implementation details required when performing a specified blur filter.

The definition of the ImageBlurFilter extension method as follows:

 public static Bitmap ImageBlurFilter(this Bitmap sourceBitmap,  
                                             BlurType blurType) 
{  
     Bitmap resultBitmap = null; 

       
     switch (blurType) 
     {  
         case BlurType.Mean3x3: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                                Matrix.Mean3x3, 1.0 / 9.0, 0); 
              } break; 
         case BlurType.Mean5x5: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                                Matrix.Mean5x5, 1.0 / 25.0, 0); 
              } break; 
         case BlurType.Mean7x7: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                                Matrix.Mean7x7, 1.0 / 49.0, 0); 
              } break; 
         case BlurType.Mean9x9: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                                Matrix.Mean9x9, 1.0 / 81.0, 0); 
              } break; 
         case BlurType.GaussianBlur3x3: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                         Matrix.GaussianBlur3x3, 1.0 / 16.0, 0); 
              } break; 
         case BlurType.GaussianBlur5x5: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                        Matrix.GaussianBlur5x5, 1.0 / 159.0, 0); 
              } break; 
         case BlurType.MotionBlur5x5: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                           Matrix.MotionBlur5x5, 1.0 / 10.0, 0); 
              } break; 
         case BlurType.MotionBlur5x5At45Degrees: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                 Matrix.MotionBlur5x5At45Degrees, 1.0 / 5.0, 0); 
              } break; 
         case BlurType.MotionBlur5x5At135Degrees: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                 Matrix.MotionBlur5x5At135Degrees, 1.0 / 5.0, 0); 
              } break; 
         case BlurType.MotionBlur7x7: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                 Matrix.MotionBlur7x7, 1.0 / 14.0, 0); 
              } break; 
         case BlurType.MotionBlur7x7At45Degrees: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                 Matrix.MotionBlur7x7At45Degrees, 1.0 / 7.0, 0); 
              } break; 
         case BlurType.MotionBlur7x7At135Degrees: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                 Matrix.MotionBlur7x7At135Degrees, 1.0 / 7.0, 0); 
              } break; 
         case BlurType.MotionBlur9x9: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                 Matrix.MotionBlur9x9, 1.0 / 18.0, 0); 
              } break; 
         case BlurType.MotionBlur9x9At45Degrees: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                 Matrix.MotionBlur9x9At45Degrees, 1.0 / 9.0, 0); 
              } break; 
         case BlurType.MotionBlur9x9At135Degrees: 
              {  
                 resultBitmap = sourceBitmap.ConvolutionFilter( 
                 Matrix.MotionBlur9x9At135Degrees, 1.0 / 9.0, 0); 
              } break; 
         case BlurType.Median3x3: 
              {  
                 resultBitmap = sourceBitmap.MedianFilter(3); 
              } break; 
         case BlurType.Median5x5: 
              {  
                 resultBitmap = sourceBitmap.MedianFilter(5); 
              } break; 
         case BlurType.Median7x7: 
              {  
                 resultBitmap = sourceBitmap.MedianFilter(7); 
              } break; 
         case BlurType.Median9x9: 
              {  
                 resultBitmap = sourceBitmap.MedianFilter(9); 
              } break; 
         case BlurType.Median11x11: 
              {  
                 resultBitmap = sourceBitmap.MedianFilter(11); 
              } break; 
      }  

   
     return resultBitmap; 
}

Daisy: Motion Blur 9×9

The Matrix class serves as a collection of various kernel definitions. The Matrix class and all public properties are defined as static. The definition of the Matrix class as follows:

     public static class Matrix 
    {  
         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[,] Mean7x7 
        {  
             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, 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[,] Mean9x9 
         {  
             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, 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, 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, 1, 1, 1, 1, 1, 1 }, }; 
            }  
        }  

    
        public static double[,] GaussianBlur3x3 
        {  
            get 
            {  
                return new double[,]   
                { {  1, 2, 1, },  
                  {  2, 4, 2, },  
                  {  1, 2, 1, }, }; 
            }  
        }  

    
        public static double[,] GaussianBlur5x5 
        {  
            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[,] MotionBlur5x5 
        {  
             get 
            {  
                return new double[,]   
                { {  1, 0, 0, 0, 1 },  
                  {  0, 1, 0, 1, 0 },  
                  {  0, 0, 1, 0, 0 }, 
                  {  0, 1, 0, 1, 0 }, 
                  {  1, 0, 0, 0, 1 }, }; 
            }  
        }  

    
        public static double[,] MotionBlur5x5At45Degrees 
        {  
             get 
            {  
                return new double[,]   
                { {  0, 0, 0, 0, 1 },  
                  {  0, 0, 0, 1, 0 },  
                  {  0, 0, 1, 0, 0 }, 
                  {  0, 1, 0, 0, 0 }, 
                  {  1, 0, 0, 0, 0 }, }; 
            }  
        }  

    
        public static double[,] MotionBlur5x5At135Degrees 
        {  
             get 
            {  
                return new double[,]   
                { {  1, 0, 0, 0, 0 },  
                  {  0, 1, 0, 0, 0 },  
                  {  0, 0, 1, 0, 0 }, 
                  {  0, 0, 0, 1, 0 }, 
                  {  0, 0, 0, 0, 1 }, }; 
            }  
        }  

    
        public static double[,] MotionBlur7x7 
        {  
            get 
            {  
                return new double[,]   
                { {  1, 0, 0, 0, 0, 0, 1 },  
                  {  0, 1, 0, 0, 0, 1, 0 },  
                  {  0, 0, 1, 0, 1, 0, 0 }, 
                  {  0, 0, 0, 1, 0, 0, 0 }, 
                  {  0, 0, 1, 0, 1, 0, 0 }, 
                  {  0, 1, 0, 0, 0, 1, 0 }, 
                  {  1, 0, 0, 0, 0, 0, 1 }, }; 
            }  
        }  

    
        public static double[,] MotionBlur7x7At45Degrees 
        {  
            get 
            {  
                return new double[,]   
                { {  0, 0, 0, 0, 0, 0, 1 },  
                  {  0, 0, 0, 0, 0, 1, 0 },  
                  {  0, 0, 0, 0, 1, 0, 0 }, 
                  {  0, 0, 0, 1, 0, 0, 0 }, 
                  {  0, 0, 1, 0, 0, 0, 0 }, 
                  {  0, 1, 0, 0, 0, 0, 0 }, 
                  {  1, 0, 0, 0, 0, 0, 0 }, }; 
            }  
        }  

    
        public static double[,] MotionBlur7x7At135Degrees 
        {  
            get 
            {  
                return new double[,]   
                { {  1, 0, 0, 0, 0, 0, 0 },  
                  {  0, 1, 0, 0, 0, 0, 0 },  
                  {  0, 0, 1, 0, 0, 0, 0 }, 
                  {  0, 0, 0, 1, 0, 0, 0 }, 
                  {  0, 0, 0, 0, 1, 0, 0 }, 
                  {  0, 0, 0, 0, 0, 1, 0 }, 
                  {  0, 0, 0, 0, 0, 0, 1 }, }; 
            }  
        }  

    
        public static double[,] MotionBlur9x9 
        {  
            get 
            {  
                return new double[,]   
                { { 1, 0, 0, 0, 0, 0, 0, 0, 1, }, 
                  { 0, 1, 0, 0, 0, 0, 0, 1, 0, }, 
                  { 0, 0, 1, 0, 0, 0, 1, 0, 0, }, 
                  { 0, 0, 0, 1, 0, 1, 0, 0, 0, }, 
                  { 0, 0, 0, 0, 1, 0, 0, 0, 0, }, 
                  { 0, 0, 0, 1, 0, 1, 0, 0, 0, }, 
                  { 0, 0, 1, 0, 0, 0, 1, 0, 0, }, 
                  { 0, 1, 0, 0, 0, 0, 0, 1, 0, }, 
                  { 1, 0, 0, 0, 0, 0, 0, 0, 1, }, }; 
            }  
        }  

    
        public static double[,] MotionBlur9x9At45Degrees 
        {  
             get 
            {  
                return new double[,]   
                { { 0, 0, 0, 0, 0, 0, 0, 0, 1, }, 
                  { 0, 0, 0, 0, 0, 0, 0, 1, 0, }, 
                  { 0, 0, 0, 0, 0, 0, 1, 0, 0, }, 
                  { 0, 0, 0, 0, 0, 1, 0, 0, 0, }, 
                  { 0, 0, 0, 0, 1, 0, 0, 0, 0, }, 
                  { 0, 0, 0, 1, 0, 0, 0, 0, 0, }, 
                  { 0, 0, 1, 0, 0, 0, 0, 0, 0, }, 
                  { 0, 1, 0, 0, 0, 0, 0, 0, 0, }, 
                  { 1, 0, 0, 0, 0, 0, 0, 0, 0, }, }; 
            }  
        }  

    
        public static double[,] MotionBlur9x9At135Degrees 
        {  
             get 
            {  
                return new double[,]   
                { { 1, 0, 0, 0, 0, 0, 0, 0, 0, }, 
                  { 0, 1, 0, 0, 0, 0, 0, 0, 0, }, 
                  { 0, 0, 1, 0, 0, 0, 0, 0, 0, }, 
                  { 0, 0, 0, 1, 0, 0, 0, 0, 0, }, 
                  { 0, 0, 0, 0, 1, 0, 0, 0, 0, }, 
                  { 0, 0, 0, 0, 0, 1, 0, 0, 0, }, 
                  { 0, 0, 0, 0, 0, 0, 1, 0, 0, }, 
                  { 0, 0, 0, 0, 0, 0, 0, 1, 0, }, 
                  { 0, 0, 0, 0, 0, 0, 0, 0, 1, }, }; 
            }  
        }  
    }

Daisy: Median 7×7

The MedianFilter extension method targets the Bitmap class. The MedianFilter method applies a Median Filter using the specified Bitmap and matrix size (window size), returning a new Bitmap representing the filtered image.

The definition of the MedianFilter extension method 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; 
}

Daisy: Motion Blur 9×9

The sample source code performs image convolution by invoking the ConvolutionFilter extension method.

The definition of the ConvolutionFilter extension method 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; 
}

Sample Images

This article features a number of sample images. All featured images have been licensed allowing for reproduction.

The sample images featuring an image of a yellow daisy is licensed under the Creative Commons Attribution-Share Alike 2.5 Generic license and can be downloaded from Wikimedia.org.

The sample images featuring an image of a white daisy is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license and can be downloaded from Wikipedia.

The sample images featuring an image of a pink daisy is licensed under the Creative Commons Attribution-Share Alike 2.5 Generic license and can be downloaded from Wikipedia.

The sample images featuring an image of a purple daisy is licensed under the Creative Commons Attribution-ShareAlike 3.0 License and can be downloaded from Wikipedia.

The Original Image

Daisy: Gaussian 3×3

Daisy: Gaussian 5×5

Daisy: Mean 3×3

Daisy: Mean 5×5

Daisy: Mean 7×7

Daisy: Mean 9×9

Daisy: Median 3×3

Daisy: Median 5×5

Daisy: Median 7×7

Daisy: Median 9×9

Daisy: Median 11×11

Daisy: Motion Blur 5×5

Daisy: Motion Blur 5×5 45 Degrees

Daisy: Motion Blur 5×5 135 Degrees

Daisy: Motion Blur 7×7

Daisy: Motion Blur 7×7 45 Degrees

Daisy: Motion Blur 7×7 135 Degrees

Daisy: Motion Blur 9×9

Daisy: Motion Blur 9×9 45 Degrees

Daisy: Motion Blur 9×9 135 Degrees

C# How to: Calculating Gaussian Kernels

Published June 8, 2013 Augmented Reality , C# , Code Samples , Extension Methods , Graphic Filters , Graphics , How to , Image Arithmetic , Image Filters , Image Processing , Microsoft , New Version , Opensource , Tip , Update 31 Comments
Tags: Augmented Reality, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, C#, Calculate Matrix, Calculating Kernels, Code Sample, Converting Images, Convolution matrix, Feature extraction, Gaussian, Gaussian Blur, Gaussian Formula, Gaussian Kernels, Graphic Filters, Image, Image Blur, Image processing, Image Smoothing, Image Transform, Machine vision, Matrix, Pixel Filters

Article Purpose

This purpose of this article is to explain and illustrate in detail the requirements involved in calculating Gaussian Kernels intended for use in image convolution when implementing Gaussian Blur filters. This article’s discussion spans from exploring concepts in theory and continues on to implement concepts through C# sample source code.

Ant: Gaussian Kernel 5×5 Weight 19

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 forms part of the accompanying sample source code, intended to implement the topics discussed and also provides the means to replicate and test the concepts being illustrated.

The sample application is a Windows Forms based application which provides functionality enabling users to generate/calculate Gaussian Kernels. Calculation results are influenced through user specified options in the form of: Kernel Size and Weight.

Ladybird: Gaussian Kernel 5×5 Weight 5.5

In the sample application and related sample source code when referring to Kernel Size, a reference is being made relating to the physical size dimensions of the kernel/matrix used in convolution. When higher values are specified in setting the Kernel Size, the resulting output image will reflect a greater degree of blurring. Kernel Sizes being specified as lower values result in the output image reflecting a lesser degree of blurring.

In a similar fashion to the Kernel size value, the Weight value provided when generating a Kernel results in smoother/more blurred images when specified as higher values. Lower values assigned to the Weight value has the expected result of less blurring being evident in output images.

Prey Mantis: Gaussian Kernel 13×13 Weight 13

The sample application has the ability to provide the user with a visual representation implementing the calculated kernel value blurring. Users are able to select source/input image from the local file system by clicking the Load Image button. When desired, users are able to save blurred/filtered images to the local file system by clicking the Save Image button.

The image below is screenshot of the Gaussian Kernel Calculator sample application in action:

Calculating Gaussian Convolution Kernels

The formula implemented in calculating Gaussian Kernels can be implemented in C# source code fairly easily. Once the method in which the formula operates has been grasped the actual code implementation becomes straight forward.

The Gaussian Kernel formula can be expressed as follows:

The formula contains a number of symbols, which define how the filter will be implemented. The symbols forming part of the Gaussian Kernel formula are described in the following list:

G(x y) – A value calculated using the Gaussian Kernel formula. This value forms part of a Kernel, representing a single element.
π – Pi, one of the better known members of the Greek alphabet. The mathematical constant defined as 22 / 7.
σ – The lower case version of the Greek alphabet letter Sigma. This symbol simply represents a threshold or factor value, as specified by the user.
e – The formula references a lower case e symbol. The symbol represents Euler’s number. The value of Euler’s number has been defined as a mathematical constant equating to 2.71828182846.
x, y – The variables referenced as x and y relate to pixel coordinates within an image. y Representing the vertical offset or row and x represents the horizontal offset or column.

Note: The formula’s implementation expects x and y to equal zero values when representing the coordinates of the pixel located in the middle of the kernel.

Ladybird: Gaussian Kernel 13×13 Weight 9.5

When calculating the kernel elements, the coordinate values expressed by x and y should reflect the distance in pixels from the middle pixel. All coordinate values must be greater than zero.

In order to gain a better grasp on the Gaussian kernel formula we can implement the formula in steps. If we were to create a 3×3 kernel and specified a weighting value of 5.5 our calculations can start off as indicated by the following illustration:

The formula has been implement on each element forming part of the kernel, 9 values in total. Coordinate values have now been replaced with actual values, differing for each position/element. Calculating zero to the power of two equates to zero. In the scenario above indicating zeros which express exponential values might help to ease initial understanding, as opposed to providing simplified values and potentially causing confusing scenarios. The following image illustrates the calculated values of each kernel element:

Ant: Gaussian Kernel 9×9 Weight 19

An important requirement to take note of at this point being that the sum total of all the elements contained as part of a kernel/matrix must equate to one. Looking at our calculated results that is not the case. The kernel needs to be modified in order to satisfy the requirement of having a sum total value of 1 when adding together all the elements of the kernel.

At this point the sum total of the kernel equates to 0.046322548968. We can correct the kernel values, ensuring the sum total of all kernel elements equate to 1. The kernel values should be updated by multiplying each element by one divided by the current kernel sum. In other words each item should be multiplied by:

1.0 / 0.046322548968

After updating the kernel by multiplying each element with the values mentioned above, the result as follows:

We have now successfully calculated a 3×3 Gaussian Blur kernel matrix which implements a weight value of 5.5. Implementing the Gaussian blur has the following effect:

Rose: Gaussian Kernel 3×3 Weight 5.5

The Original Image

The calculated Gaussian Kernel can now be implemented when performing image convolution.

Implementing Gaussian Kernel Calculations

In this section of the article we will be exploring how to implement Gaussian Blur kernel calculations in terms of C# code. Defined as part of the sample source code the definition of the static MatrixCalculator class, exposing the static Calculate method. All of the formula calculation tasks discussed in the previous section have been implemented within this method.

As parameter values the method expects a value indicating the kernel size and a value representing the Weight value. The Calculate method returns a two dimensional array of type double. The return value array represents the calculated kernel.

The definition of the MatrixCalculator.Calculate method as follows:

public static double[,] Calculate(int length, double weight) 
{
    double[,] Kernel = new double [length, lenght]; 
    double sumTotal = 0; 

  
    int kernelRadius = lenght / 2; 
    double distance = 0; 

  
    double calculatedEuler = 1.0 /  
    (2.0 * Math.PI * Math.Pow(weight, 2)); 

  
    for (int filterY = -kernelRadius; 
         filterY <= kernelRadius; filterY++) 
    {
        for (int filterX = -kernelRadius; 
            filterX <= kernelRadius; filterX++) 
        {
            distance = ((filterX * filterX) +  
                       (filterY * filterY)) /  
                       (2 * (weight * weight)); 

  
            Kernel[filterY + kernelRadius,  
                   filterX + kernelRadius] =  
                   calculatedEuler * Math.Exp(-distance); 

  
            sumTotal += Kernel[filterY + kernelRadius,  
                               filterX + kernelRadius]; 
        } 
    } 

  
    for (int y = 0; y < lenght; y++) 
    { 
        for (int x = 0; x < lenght; x++) 
        { 
            Kernel[y, x] = Kernel[y, x] *  
                           (1.0 / sumTotal); 
        } 
    } 

  
    return Kernel; 
}

Ladybird: Gaussian Kernel 19×19 Weight 9.5

The sample source code provides the definition of the ConvolutionFilter extension method, targeting the Bitmap class. This method accepts as a parameter a two dimensional array representing the matrix kernel to implement when performing image convolution. The matrix kernel value passed to this function originates from the calculated Gaussian kernel.

Detailed below is the definition of the ConvolutionFilter extension method:

public 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 : blue)); 

   
            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; 
}

Ant: Gaussian Kernel 7×7 Weight 19

Sample Images

This article features a number of sample images. All featured images have been licensed allowing for reproduction.

The sample images featuring an image of a prey mantis is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license and can be downloaded from Wikipedia.

The sample images featuring an image of an ant has been released into the public domain by its author, Sean.hoyland. This applies worldwide. In some countries this may not be legally possible; if so: Sean.hoyland grants anyone the right to use this work for any purpose, without any conditions, unless such conditions are required by law. The original image can be downloaded from Wikipedia.

The sample images featuring an image of a ladybird (ladybug or lady beetle) is licensed under the Creative Commons Attribution-Share Alike 2.0 Generic license and can be downloaded from Wikipedia.

The sample images featuring an image of a wasp is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license and can be downloaded from Wikimedia.org.

The Original Image

Wasp Gaussian Kernel 3×3 Weight 9.25

Wasp Gaussian Kernel 5×5 Weight 9.25

Wasp Gaussian Kernel 7×7 Weight 9.25

Wasp Gaussian Kernel 9×9 Weight 9.25

Wasp Gaussian Kernel 11×11 Weight 9.25

Wasp Gaussian Kernel 13×13 Weight 9.25

Wasp Gaussian Kernel 15×15 Weight 9.25

Wasp Gaussian Kernel 17×17 Weight 9.25

Wasp Gaussian Kernel 19×19 Weight 9.25

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