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C# How to: Morphological Edge Detection

Article purpose

The objective of this article is to explore Image Edge Detection implemented by means of Image Dilation and Image Erosion morphological filters. In addition we explore the concept of Image Sharpening implementing morphological edge detection.

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 implement all of the concepts illustrated throughout this article. Using the sample application users can easily test and replicate concepts.

Clicking the Load Image button allows users to select source/input images from the local file system. Filter option categories are: Colour(s), morphology type, edge options and filter size.

This article and sample source code can process colour images as source images. The user can specify which colour components to include in resulting images. The three checkboxes labelled Red, Green and Blue indicate whether the related colour component features in result images.

The four radiobuttons labelled Dilate, Erode, Open and Closed enable the user to select the type of morphological filter to apply.

Edge detection options include: None, Edge Detection and Image Sharpening. Selecting None results in only the selected morphological filter being applied.

Filter sizes range from 3×3 up to 17×17. The filter size specified determines the intensity of the morphological filter applied.

If desired users are able to save filter result images to the local file system by clicking the Save Image button. The image below is a screenshot of the Morphological Edge Detection sample application in action:

Morphology – Image Erosion and Dilation

Image Erosion and Dilation are implementations of morphological filters, a subset of Mathematical Morphology. In simpler terms Image Dilation can be defined by this quote:

Dilation is one of the two basic operators in the area of mathematical morphology, the other being erosion. It is typically applied to binary images, but there are versions that work on grayscale images. The basic effect of the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels (i.e. white pixels, typically). Thus areas of foreground pixels grow in size while holes within those regions become smaller.

Image Erosion being a related concept is defined by this quote:

Erosion is one of the two basic operators in the area of mathematical morphology, the other being dilation. It is typically applied to binary images, but there are versions that work on grayscale images. The basic effect of the operator on a binary image is to erode away the boundaries of regions of foreground pixels (i.e. white pixels, typically). Thus areas of foreground pixels shrink in size, and holes within those areas become larger.

From the definitions listed above we gather that Image Dilation increases the size of edges contained in an image. In contrast Image Erosion decreases or shrinks the size of an Image’s edges.

Image Edge Detection

We gain a good definition of edge detection from Wikipedia’s article on edge detection:

Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in 1D signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction.

In this article we implement edge detection based on the type of morphology being performed. In the case of Image Erosion the eroded image is subtracted from the original image resulting in an image with pronounced edges. When implementing Image Dilation, edge detection is achieved by subtracting the original image from the dilated image.

Image Sharpening

Image Sharpening is often referred to by the term Edge Enhancement, from Wikipedia we gain the following definition:

Edge enhancement is an image processing filter that enhances the edge contrast of an image or video in an attempt to improve its acutance (apparent sharpness).

The filter works by identifying sharp edge boundaries in the image, such as the edge between a subject and a background of a contrasting color, and increasing the image contrast in the area immediately around the edge. This has the effect of creating subtle bright and dark highlights on either side of any edges in the image, called overshoot and undershoot, leading the edge to look more defined when viewed from a typical viewing distance.

In this article we implement Image Sharpening by first creating an edge detection image which we then add to the original image, resulting in an image with enhanced edges.

Implementing Morphological Filters

The sample source code provides the definition of the DilateAndErodeFilter extension method targeting the Bitmap class. The DilateAndErodeFilter extension method as a single method implementation is capable of applying a specified morphological filter, edge detection and Image Sharpening. The following code snippet details the implementation of the the DilateAndErodeFilter extension method:

public static Bitmap DilateAndErodeFilter(this Bitmap sourceBitmap,  
                                        int matrixSize, 
                                        MorphologyType morphType, 
                                        bool applyBlue = true, 
                                        bool applyGreen = true, 
                                        bool applyRed = true,
                                        MorphologyEdgeType edgeType = 
                                        MorphologyEdgeType.None)  
{ 
    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; 

  
    int blue = 0; 
    int green = 0; 
    int red = 0; 

  
    byte morphResetValue = 0; 

  
    if (morphType == MorphologyType.Erosion) 
    { 
        morphResetValue = 255; 
    } 

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

  
            blue = morphResetValue; 
            green = morphResetValue; 
            red = morphResetValue; 

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

  
                        if (pixelBuffer[calcOffset] > blue) 
                        {
                            blue = pixelBuffer[calcOffset]; 
                        } 

  
                        if (pixelBuffer[calcOffset + 1] > green) 
                        {
                            green = pixelBuffer[calcOffset + 1]; 
                        } 

  
                        if (pixelBuffer[calcOffset + 2] > red) 
                        { 
                            red = pixelBuffer[calcOffset + 2]; 
                        } 
                    } 
                }
            } 
            else if (morphType == MorphologyType.Erosion) 
            { 
                for (int filterY = -filterOffset; 
                    filterY <= filterOffset; filterY++) 
                { 
                    for (int filterX = -filterOffset; 
                        filterX <= filterOffset; filterX++) 
                    { 
                        calcOffset = byteOffset + 
                                     (filterX * 4) + 
                        (filterY * sourceData.Stride); 

  
                        if (pixelBuffer[calcOffset] < blue) 
                        {
                            blue = pixelBuffer[calcOffset]; 
                        } 

  
                        if (pixelBuffer[calcOffset + 1] < green) 
                        { 
                            green = pixelBuffer[calcOffset + 1]; 
                        } 

  
                        if (pixelBuffer[calcOffset + 2] < red) 
                        {
                            red = pixelBuffer[calcOffset + 2]; 
                        } 
                    }
                } 
            }

  
            if (applyBlue == false ) 
            {
                blue = pixelBuffer[byteOffset]; 
            } 

  
            if (applyGreen == false ) 
            { 
                green = pixelBuffer[byteOffset + 1]; 
            } 

  
            if (applyRed == false ) 
            { 
                red = pixelBuffer[byteOffset + 2]; 
            } 

  
            if (edgeType == MorphologyEdgeType.EdgeDetection ||  
                edgeType == MorphologyEdgeType.SharpenEdgeDetection) 
            {
                if (morphType == MorphologyType.Dilation) 
                {
                    blue = blue - pixelBuffer[byteOffset]; 
                    green = green - pixelBuffer[byteOffset + 1]; 
                    red = red - pixelBuffer[byteOffset + 2]; 
                } 
                else if (morphType == MorphologyType.Erosion) 
                { 
                    blue = pixelBuffer[byteOffset] - blue; 
                    green = pixelBuffer[byteOffset + 1] - green; 
                    red = pixelBuffer[byteOffset + 2] - red; 
                } 

  
                if (edgeType == MorphologyEdgeType.SharpenEdgeDetection) 
                { 
                    blue += pixelBuffer[byteOffset]; 
                    green += pixelBuffer[byteOffset + 1]; 
                    red += pixelBuffer[byteOffset + 2]; 
                } 
            } 

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

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

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

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

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

  
    resultBitmap.UnlockBits(resultData); 

  
    return resultBitmap; 
}

Sample Images

The source/input image used in this article is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license and can be downloaded from Wikipedia: http://en.wikipedia.org/wiki/File:Bathroom_with_bathtube.jpg

Original Image

Erosion 3×3, Edge Detect, Red, Green and Blue

Erosion 3×3, Edge Detect, Blue

Erosion 3×3, Edge Detect, Green and Blue

Erosion 3×3, Edge Detect, Red

Erosion 3×3, Edge Detect, Red and Blue

Erosion 3×3, Edge Detect, Red and Green

Erosion 7×7, Sharpen, Red, Green and Blue

Erosion 7×7, Sharpen, Blue

Erosion 7×7, Sharpen, Green

Erosion 7×7, Sharpen, Green and Blue

Erosion 7×7, Sharpen, Red

Erosion 7×7, Sharpen, Red and Blue

Erosion 7×7, Sharpen, Red and Green

C# How to: Image Erosion and Dilation

Published May 19, 2013 Augmented Reality , C# , Code Samples , Extension Methods , Graphic Filters , Graphics , How to , Image Convolution , Image Filters , Learn Everyday , Microsoft , Morphology Filters , New Version , Opensource , Tip 31 Comments
Tags: Augmented Reality, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, bitmapda, BitmapData, C#, Code Sample, Computer vision, Convolution filters, Convolution Kernel, Convolution matrix, Feature extraction, Graphic Filters, Image, Image processing, Image Transform, Machine vision, Matrix, Pixel Filters, Pixel Manipulation

Article purpose

The purpose of this article is aimed at exploring the concepts of Image Erosion, Image Dilation, Open Morphology and Closed Morphology. In addition this article extends conventional Image Erosion and Dilation implementations through partial colour variations of Image Erosion and Dilation.

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

Included in this article’s sample source code you’ll find a Windows Forms based sample application. The sample application can be used to test and replicate the concepts we explore in this article.

When executing the sample application source/input images can selected from the local file system by clicking the Load Image button. On the right-hand side of the sample application’s user interface users can adjust the provided controls in order to modify the method of filtering being implemented.

The three CheckBoxes labelled Red, Green and Blue relate to whether the relevant colour component will be regarded or not when implementing the configured image filter.

Users are required to select an image filter: Image Erosion, Image Dilation Open Morphology or Closed Morphology. The interface selection is expressed by means of four RadioButtons respectively labelled Dilate, Erode, Open and Closed.

The only other input required from a user comes in the form of selecting the filter intensity/filter size. The dropdown ComboxBox indicated as Filter Size provides the user with several intensity levels ranging from 3×3 to 17×17. Note: Larger filter sizes result in additional processing required when implementing the filter. Large images set to implement large sized filters may require more processor cycles.

Resulting filtered images can be saved to the local file system by clicking the Save Image button. The screenshot below illustrates the Image Erosion and Dilation sample application in action:

Mathematical Morphology

A description of Mathematical Morphology as expressed on Wikipedia:

Mathematical morphology (MM) is a theory and technique for the analysis and processing of geometrical structures, based on set theory, lattice theory, topology, and random functions. MM is most commonly applied to digital images, but it can be employed as well on graphs, surface meshes, solids, and many other spatial structures.

Topological and geometrical continuous-space concepts such as size, shape, convexity, connectivity, and geodesic distance, were introduced by MM on both continuous and discrete spaces. MM is also the foundation of morphological image processing, which consists of a set of operators that transform images according to the above characterizations.

MM was originally developed for binary images, and was later extended to grayscale functions and images. The subsequent generalization to complete lattices is widely accepted today as MM’s theoretical foundation.

In this article we explore Image Erosion, Dilation, as well as Open and Closed Morphology. The implementation of these image filters are significantly easier to grasp when compared to most definitions of Mathematical Morphology.

Image Erosion and Dilation

Image Erosion and Dilation are implementations of morphological filters, a subset of Mathematical Morphology. In simpler terms Image Dilation can be defined by this quote:

Dilation is one of the two basic operators in the area of mathematical morphology, the other being erosion. It is typically applied to binary images, but there are versions that work on grayscale images. The basic effect of the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels (i.e. white pixels, typically). Thus areas of foreground pixels grow in size while holes within those regions become smaller.

Image Erosion being a related concept is defined by this quote:

Erosion is one of the two basic operators in the area of mathematical morphology, the other being dilation. It is typically applied to binary images, but there are versions that work on grayscale images. The basic effect of the operator on a binary image is to erode away the boundaries of regions of foreground pixels (i.e. white pixels, typically). Thus areas of foreground pixels shrink in size, and holes within those areas become larger.

From the definitions listed above we gather that Image Dilation increases the size of edges contained in an image. In contrast Image Erosion decreases or shrinks the size of an Image’s edges.

Open and Closed Morphology

Building upon the concepts of Image Erosion and Dilation this section explores Open and Closed Morphology. A good definition of Open Morphology can be expressed as follows:

The basic effect of an opening is somewhat like erosion in that it tends to remove some of the foreground (bright) pixels from the edges of regions of foreground pixels. However it is less destructive than erosion in general. As with other morphological operators, the exact operation is determined by a structuring element. The effect of the operator is to preserve foreground regions that have a similar shape to this structuring element, or that can completely contain the structuring element, while eliminating all other regions of foreground pixels.

In turn Closed Morphology can be defined as follows:

Closing is similar in some ways to dilation in that it tends to enlarge the boundaries of foreground (bright) regions in an image (and shrink background color holes in such regions), but it is less destructive of the original boundary shape. As with other morphological operators, the exact operation is determined by a structuring element. The effect of the operator is to preserve background regions that have a similar shape to this structuring element, or that can completely contain the structuring element, while eliminating all other regions of background pixels.

Implementing Image Erosion and Dilation

In this article we implement Image Erosion and Dilation by iterating each pixel contained within an image. The colour of each pixel is determined by taking into regard a pixel’s neighbouring pixels.

When implementing Image Dilation a pixel’s value is determined by comparing neighbouring pixels’ colour values, determining the highest colour value expressed amongst neighbouring pixels.

In contrast to Image Dilation we implement Image Erosion by also inspecting neighbouring pixels’ colour values, determining the lowest colour value expressed amongst neighbouring pixels.

In addition to conventional Image Erosion and Dilation the sample source code provides the ability to perform Image Erosion and Dilation targeting only specific colour components. The result of specific colour Image Erosion and Dilation produces images which express the effects of Image Erosion and Dilation only in certain colours. Depending on filter parameters specified Image edges appear to have a coloured glow or shadow.

The sample source code provides the definition for the DilateAndErodeFilter extension method, targeting the Bitmap class. The following code snippet details the implementation of the DilateAndErodeFilter extension method:

public static Bitmap DilateAndErodeFilter(
                           this Bitmap sourceBitmap,  
                           int matrixSize, 
                           MorphologyType morphType, 
                           bool applyBlue = true, 
                           bool applyGreen = true, 
                           bool applyRed = true )  
{
    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; 

   
    byte blue = 0; 
    byte green = 0; 
    byte red = 0; 

   
    byte morphResetValue = 0; 

   
    if (morphType == MorphologyType.Erosion) 
    { 
        morphResetValue = 255; 
    } 

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

   
            blue = morphResetValue; 
            green = morphResetValue; 
            red = morphResetValue; 

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

   
                        if (pixelBuffer[calcOffset] > blue) 
                        {
                            blue = pixelBuffer[calcOffset]; 
                        }

   
                        if (pixelBuffer[calcOffset + 1] > green) 
                        { 
                            green = pixelBuffer[calcOffset + 1]; 
                        } 

   
                        if (pixelBuffer[calcOffset + 2] > red) 
                        {
                            red = pixelBuffer[calcOffset + 2]; 
                        } 
                    } 
                }
            }
            else if (morphType == MorphologyType .Erosion) 
            {
                for (int filterY = -filterOffset; 
                    filterY <= filterOffset; filterY++) 
                { 
                    for (int filterX = -filterOffset; 
                        filterX <= filterOffset; filterX++) 
                    {
                        calcOffset = byteOffset + 
                                     (filterX * 4) + 
                        (filterY * sourceData.Stride); 

   
                        if (pixelBuffer[calcOffset] < blue) 
                        { 
                            blue = pixelBuffer[calcOffset]; 
                        } 

   
                        if (pixelBuffer[calcOffset + 1] < green) 
                        { 
                            green = pixelBuffer[calcOffset + 1]; 
                        }

   
                        if (pixelBuffer[calcOffset + 2] < red) 
                        { 
                            red = pixelBuffer[calcOffset + 2]; 
                        }
                    }
                } 
            }

   
            if (applyBlue == false ) 
            { 
                blue = pixelBuffer[byteOffset]; 
            } 

   
            if (applyGreen == false ) 
            {
                green = pixelBuffer[byteOffset + 1]; 
            }

   
            if (applyRed == false ) 
            {
                red = pixelBuffer[byteOffset + 2]; 
            }

   
            resultBuffer[byteOffset] = blue; 
            resultBuffer[byteOffset + 1] = green; 
            resultBuffer[byteOffset + 2] = 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; 
}

Implementing Open and Closed Morphology

The sample source code implements Open Morphology by first implementing Image Erosion on a source image, the resulting image is then filtered by implementing Image Dilation.

In a reverse fashion Closed Morphology is achieved by first implementing Image Dilation on a source image, which is then further filtered by implementing Image Erosion.

The sample source code defines the OpenMorphologyFilter and CloseMorphologyFilter extension methods, both targeting the Bitmap class. The implementation as follows:

public static Bitmap OpenMorphologyFilter(
                            this Bitmap sourceBitmap, 
                            int matrixSize,
                            bool applyBlue = true, 
                            bool applyGreen = true, 
                            bool applyRed = true ) 
{ 
    Bitmap resultBitmap = 
           sourceBitmap.DilateAndErodeFilter(
                        matrixSize, MorphologyType.Erosion, 
                        applyBlue, applyGreen, applyRed); 

   
    resultBitmap = 
          resultBitmap.DilateAndErodeFilter(
                       matrixSize,  
                       MorphologyType.Dilation,  
                       applyBlue, applyGreen, applyRed); 

   
    return resultBitmap; 
} 

   
public static Bitmap CloseMorphologyFilter(
                            this Bitmap sourceBitmap, 
                            int matrixSize,
                            bool applyBlue = true, 
                            bool applyGreen = true, 
                            bool applyRed = true ) 
{ 
    Bitmap resultBitmap = 
           sourceBitmap.DilateAndErodeFilter(
                        matrixSize, MorphologyType.Dilation, 
                        applyBlue, applyGreen, applyRed); 

   
    resultBitmap = 
          resultBitmap.DilateAndErodeFilter(
                       matrixSize,  
                       MorphologyType.Erosion,
                       applyBlue, applyGreen, applyRed); 

   
    return resultBitmap; 
}

Sample Images

The original source image used to create all of the sample images in this article has been licensed under the Creative Commons Attribution-Share Alike 3.0 Unported, 2.5 Generic, 2.0 Generic and 1.0 Generic license. The original image is attributed to Kenneth Dwain Harrelson and can be downloaded from Wikipedia.

The Original Image

Image Dilation 3×3 Blue

Image Dilation 3×3 Blue, Green

Image Dilation 3×3 Green

Image Dilation 3×3 Red

Image Dilation 3×3 Red, Blue

Image Dilation 3×3 Red, Green, Blue

Image Dilation 13×13 Blue

Image Erosion 3×3 Green, Blue

Image Erosion 3×3 Green

Image Erosion 3×3 Red

Image Erosion 3×3 Red, Blue

Image Erosion 3×3 Red, Green

Image Erosion 3×3 Red, Green, Blue

Image Erosion 9×9 Green

Image Erosion 9×9 Red

Image Open Morphology 11×11 Green

Image Open Morphology 11×11 Green Blue

Image Open Morphology 11×11 Red

Image Open Morphology 11×11 Red, Blue

C# How to: Image Colour Average

Published May 18, 2013 Augmented Reality , C# , Code Samples , Extension Methods , Graphic Filters , Graphics , How to , Image Filters , Learn Everyday , Microsoft , New Version , Opensource , Tip 31 Comments
Tags: Alpha Red Green Blue, ARGB, Augmented Reality, Binary Image, Bitmap, Bitmap ARGB, Bitmap ARGB Array, Bitmap Filters, Bitmap.LockBits, BitmapData, BitmapData.Stride, C#, C# Convolution, Code Sample, Color Filters, Color Substitution, Computer vision, Converting Bitmaps, Converting Images, Convolution filters, Convolution Kernel, Convolution matrix, Extension Methods, Feature extraction, Graphic Filters, Image, Image Average, Image Blending, Image Manipulation, Image processing, Image Transform, Machine vision, Matrix, Open source, Pixel Filters, Pixel Manipulation, Windows Forms

Article purpose

This article’s intension is focussed on providing a discussion on the tasks involved in implementing Image Colour Averaging. Pixel colour averages are calculated from neighbouring pixels.

Sample source code

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

Using the Sample Application

The sample source code associated with this article includes a Windows Forms based sample application. The sample application is provided with the intention of illustrating the concepts explored in this article. In addition the sample application serves as a means of testing and replicating results.

By clicking the Load Image button users are able to select input/source images from the local file system. On the right hand side of the screen various controls enable the user to control the implementation of colour averaging. The three CheckBoxes labelled Red, Green and Blue relates to whether an individual colour component is to be included in calculating colour averages.

The filter intensity can be specified through selecting a filter size from the dropdown ComboBox, specifying higher values will result in output images expressing more colour averaging intensity.

Additional image filter effects can be achieved through implementing colour component shifting/swapping. When colour components are shifted left the result will be:

Blue is set to the original value of the Red component.
Red is set to the original value of the Green component.
Green is set to the original value of the Blue component.

When colour components are shifted right the result will be:

Red is set to the original value of the Blue component
Blue is set to the original value of the Green component
Green is set to the original value of the Red Component

Resulting images can be saved by the user to the local file system by clicking the Save Image button. The following image is a screenshot of the Image Colour Average sample application in action:

Averaging Colours

In this article and the accompanying sample source code colour averaging is implemented on a per pixel basis. An average colour value is calculated based on a pixel’s neighbouring pixels’ colour. Determining neighbouring pixels in the sample source code has been implemented in much the same method as image convolution. The major difference to image convolution is the absence of a fixed matrix/kernel.

Additional resulting visual effects can be achieved through various options/settings implemented whilst calculating colour averages. Additional options include being able to specify which colour component averages to implement. Furthermore colour components can be swapped/shifted around.

The sample source code implements the AverageColoursFilter extension method, targeting the Bitmap class. The extent or degree to which colour averaging will be evident in resulting images can be controlled through specifying different values set to the matrixSize parameter. The matrixSize parameter in essence determines the number of neighbouring pixels involved in calculating an average colour.

The individual pixel colour components Red, Green and Blue can either be included or excluded in calculating averages. The three method boolean parameters applyBlue, applyGreen and applyRed will determine an individual colour components inclusion in averaging calculations. If a colour component is to be excluded from averaging the resulting image will instead express the original source/input image’s colour component.

The intensity of a specific colour component average can be applied to another colour component by means of swapping/shifting colour components, which is indicated through the shiftType method parameter.

The following code snippet provides the implementation of the AverageColoursFilter extension method:

public static Bitmap AverageColoursFilter(
                            this Bitmap sourceBitmap,  
                            int matrixSize,   
                            bool applyBlue = true, 
                            bool applyGreen = true, 
                            bool applyRed = true, 
                            ColorShiftType shiftType = 
                            ColorShiftType.None)  
{ 
    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; 

   
    int blue = 0; 
    int green = 0; 
    int red = 0; 

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

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

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

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

   
            blue = blue / matrixSize; 
            green = green / matrixSize; 
            red = red / matrixSize; 

   
            if (applyBlue == false) 
            { 
                blue = pixelBuffer[byteOffset]; 
            }

   
            if (applyGreen == false) 
            {
                green = pixelBuffer[byteOffset + 1]; 
            }

   
            if (applyRed == false) 
            { 
                red = pixelBuffer[byteOffset + 2]; 
            } 

   
            if (shiftType == ColorShiftType.None) 
            {
                resultBuffer[byteOffset] = (byte)blue; 
                resultBuffer[byteOffset + 1] = (byte)green; 
                resultBuffer[byteOffset + 2] = (byte)red; 
                resultBuffer[byteOffset + 3] = 255; 
            }
            else if (shiftType == ColorShiftType.ShiftLeft) 
            {
                resultBuffer[byteOffset] = (byte)green; 
                resultBuffer[byteOffset + 1] = (byte)red; 
                resultBuffer[byteOffset + 2] = (byte)blue; 
                resultBuffer[byteOffset + 3] = 255; 
            } 
            else if (shiftType == ColorShiftType.ShiftRight) 
            {
                resultBuffer[byteOffset] = (byte)red; 
                resultBuffer[byteOffset + 1] = (byte)blue; 
                resultBuffer[byteOffset + 2] = (byte)green; 
                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; 
}

The definition of the ColorShiftType enum:

public enum ColorShiftType  
{
    None, 
    ShiftLeft, 
    ShiftRight 
}

Sample Images

The original image used in generating the sample images that form part of this article, has been licensed under the Creative Commons Attribution-Share Alike 3.0 Unported, 2.5 Generic, 2.0 Generic and 1.0 Generic license. The original image can be downloaded from Wikipedia.

Original Image

Colour Average Blue Size 11

Colour Average Blue Size 11 Shift Left

Colour Average Blue Size 11 Shift Right

Colour Average Green Size 11 Shift Right

Colour Average Green, Blue Size 11

Colour Average Green, Blue Size 11 Shift Left

Colour Average Green, Blue Size 11 Shift Right

Colour Average Red Size 11

Colour Average Red Size 11 Shift Left

Colour Average Red, Blue Size 11

Colour Average Red, Blue Size 11 Shift Left

Colour Average Red, Green Size 11

Colour Average Red, Green Size 11 Shift Left

Colour Average Red, Green Size 11 Shift Right

Colour Average Red, Green, Blue Size 11

Colour Average Red, Green, Blue Size 11 Shift Left

Colour Average Red, Green, Blue Size 11 Shift Right

C# How to: Image Unsharp Mask

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

Article purpose

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

Sample Source code

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

Using the Sample Application

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

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

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

What is Image Unsharp Masking?

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

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

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

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

Applying a Convolution Matrix filter

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

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

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

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

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

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

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

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

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

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

  
     int byteOffset = 0; 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

   
     return resultBitmap; 
}

Subtracting and Adding Images

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  
    resultBitmap.UnlockBits(resultData); 

  
    return resultBitmap; 
}

Matrix Definition

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

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

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

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

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

Implementing Image Unsharpening

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

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

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

   
    return resultBitmap; 
 } 

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

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

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

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

   
    return resultBitmap; 
}

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

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

   
    return resultBitmap; 
}

Sample Images

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

The Original Image

Unsharp Gaussian 3×3

Unsharp Gaussian 5×5

Unsharp Mean 3×3

Unsharp Gaussian 5×5

C# How to: Image Median Filter

Published May 18, 2013 Augmented Reality , C# , Code Samples , Extension Methods , Graphic Filters , Graphics , How to , Image Convolution , Image Filters , Microsoft , Opensource 32 Comments
Tags: Augmented Reality, Binary Image, Bitmap, Bitmap ARGB, Bitmap ARGB Array, Bitmap Filters, Bitmap.LockBits, BitmapData, C#, Code Sample, Computer vision, Extension Methods, Image, Image Filters, Image Manipulation, Image Noise, Image processing, Median Filter, Pixel Filters, Pixel Manipulation, Signal Processing

Article purpose

The objective of this article is focussed on providing a discussion on implementing a Median Filter on an image. This article illustrates varying levels of filter intensity: 3×3, 5×5, 7×7, 9×9, 11×11 and 13×13.

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 concepts explored in this article can be easily replicated by making use of the Sample Application, which forms part of the associated sample source code accompanying this article.

When using the Image Median Filter sample application you can specify a input/source image by clicking the Load Image button. The dropdown combobox towards the bottom middle part of the screen relates the various levels of filter intensity.

If desired a user can save the resulting filtered image to the local file system by clicking the Save Image button.

The following image is screenshot of the Image Median Filter sample application in action:

What is a Median Filter

From Wikipedia we gain the following quotes:

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 (but see discussion below).

The main idea of the median filter is to run through the signal entry by entry, replacing each entry with the median of neighboring entries. The pattern of neighbors is called the "window", which slides, entry by entry, over the entire signal. For 1D signals, the most obvious window is just the first few preceding and following entries, whereas for 2D (or higher-dimensional) signals such as images, more complex window patterns are possible (such as "box" or "cross" patterns). Note that if the window has an odd number of entries, then the median is simple to define: it is just the middle value after all the entries in the window are sorted numerically. For an even number of entries, there is more than one possible median, see median for more details.

In simple terms, a Median Filter can be applied to images in order to achieve image smoothing or image noise reduction. The Median Filter in contrast to most image smoothing methods, to a degree exhibits edge preservation properties.

Applying a Median Filter

The sample source code defines the MedianFilter extension method targeting the Bitmap class. The matrixSize parameter determines the intensity of the Median Filter being applied.

The MedianFilter extension method iterates each pixel of the source image. When iterating image pixels we determine the neighbouring pixels of the pixel currently being iterated. After having built up a list of neighbouring pixels, the List is then sorted and from there we determine the middle pixel value. The final step involves assigning the determined middle pixel to the current pixel in the resulting image, represented as an array of pixel colour component bytes.

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

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

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

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

  
    sourceBitmap.UnlockBits(sourceData); 

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

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

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

  
    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; 
}

Sample Images

The sample images illustrated in this article were rendered from the same source image which is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported, 2.5 Generic, 2.0 Generic and 1.0 Generic license. The original image is attributed to Luc Viatour – www.Lucnix.be and can be downloaded from Wikipedia.

The Original Source Image

Median 3×3 Filter

Median 5×5 Filter

Median 7×7 Filter

Median 9×9 Filter

Median 11×11 Filter

Median 13×13 Filter

Posts Tagged 'Pixel Manipulation'

Article purpose

Sample source code

Using the sample application

Morphology – Image Erosion and Dilation

Image Edge Detection

Image Sharpening

Implementing Morphological Filters

Sample Images

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Using the sample application

Mathematical Morphology

Image Erosion and Dilation

Open and Closed Morphology

Implementing Image Erosion and Dilation

Implementing Open and Closed Morphology

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Sample source code

Using the Sample Application

Averaging Colours

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Sample Source code

Using the Sample Application

What is Image Unsharp Masking?

Applying a Convolution Matrix filter

Subtracting and Adding Images

Matrix Definition

Implementing Image Unsharpening

Sample Images

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Sample source code

Using the Sample Application

What is a Median Filter

Applying a Median Filter

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Dewald Esterhuizen

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