Posts Tagged 'Median Filter'

C# How to: Min/Max Edge Detection

Article Purpose

This article serves as a detailed discussion on implementing  through maximum and minimum value subtraction. Additional concepts illustrated in this article include implementing a and RGB conversion.

Frog Filter 3×3 Smoothed

Frog Filter 3x3 Smoothed

Sample Source Code

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

Min Max Edge Detection Sample Source Code

Using the Sample Application

This article’s accompanying sample source code includes a based sample application. The sample application provides an implementation of the concepts explored by this article. Concepts discussed can be easily replicated and tested by using the sample application.

Source/input files can be specified from the local system when clicking the Load Image button. Additionally users also have the option to save resulting filtered by clicking the Save Image button.

The sample application user interface enables the user  to specify three filter configuration values. These values serve as input parameters to the Min/Max Edge Detection Filter and can be described as follows:

  • Filter Size – Determines the number of surrounding to consider when calculating the minimum and maximum pixel values. This value equates to the size of a pixel’s neighbourhood of pixels. When gradient edges expressed in a source image requires a higher or lower level of expression in result images the filter size value should be adjusted. Higher Filter Size values result in gradient edges being expressed at greater intensity levels in resulting images. Inversely, lower Filter Size values delivers the opposite result of gradient edges being expressed at lesser intensity levels.
  • Smooth Noise – when present in source images, can to varying degrees affect the Min/Max Edge Detection Filter’s accuracy in calculating gradient edges. In order to reduce the negative affects of source image noise an image smoothing filter may be implemented. Smoothing out image noise requires additional filter processing and therefore requires additional computation time. If source images reflect minor or no image noise, additional image smoothing may be excluded to reduce filter processing duration.
  • Grayscale – When required, result images can be expressed in grayscale through configuring this value.

The following image represents a screenshot of the Min/Max Edge Detection Sample application in action.

Min Max Edge Detection Sample Application

Min/Max

The method of illustrated in this article can be classified as a variation of commonly implemented edge detection methods. expressed within a source can be determined through the presence of sudden and significant changes in gradient levels that occur within a small/limited perimeter.

As a means to determine gradient level changes the Min/Max Edge Detection algorithm performs inspection, comparing maximum and minimum colour channel values. Should the difference between maximum and minimum colour values be significant, it would be an indication of a significant change in gradient level within the being inspected.

Image noise represents interference in relation to regular gradient level expression. Image noise does not signal the presence of an , although could potentially result in incorrectly determining image edge presence. Image noise and the negative impact thereof can be significantly reduced when applying image smoothing, also sometimes referred to as . The Min/Max Edge Detection algorithm makes provision for optional image smoothing implemented in the form of a filter.

The following sections provide more detail regarding the concepts introduced in this section, pixel neighbourhood and median filter.

Frog Filter 3×3 Smoothed

Frog Filter 3x3 Smoothed

Pixel Neighbourhood

A refers to a set of pixels, all of which are related through location coordinates. The width and height of a must be equal, in other words, a pixel neighbourhood can only be square. Additionally, the width/height of a pixel neighbourhood must be an uneven value. When inspecting a pixel’s neighbouring pixels, the pixel being inspected will always be located at the exact center of the . Only when a pixel neighbourhood’s width/height are an uneven value can such a have an exact center pixel. Each pixel represented in an has a different set of neighbours, some neighbours overlap, but no two pixels have the exact same neighbours. A pixel’s neighbouring pixels can be determined when considering the pixel to be at the center of a block of pixels, extending half the neighbourhood size less one in horizontal, vertical and diagonal directions.

Median Filter

In context of this article and the Min/Max Edge Detection filter, median filtering has been implemented as a means to reduce source image noise. From the we gain the following quote:

In statistics and probability theory, the median is the number separating the higher half of a data sample, a population, or a probability distribution, from the lower half. The median of a finite list of numbers can be found by arranging all the observations from lowest value to highest value and picking the middle one (e.g., the median of {3, 3, 5, 9, 11} is 5).

Frog Filter 3×3 Smoothed

Frog2_Filter3x3_Smoothed

The application of a filter is based in the concept of as discussed earlier. The implementation steps required when applying a median filter can be described as follows:

  1. Iterate every pixel. The of each pixel in a source image needs to be determined and inspected.
  2. Order/Sort pixel neighbourhood values. Once a pixel neighbourhood for a specific pixel has been determined the values expressed by all the pixels in that neighbourhood needs to be sorted or ordered according to value.
  3. Determine midpoint value. In relation to the sorted values, the value positioned exactly halfway between first and last value needs to be determined. As an example, if a pixel neighbourhood contains a total of nine pixels, the midpoint would be at position number five, which is four positions from the first and last value inclusive. The midpoint value in a sorted range of neighbourhood pixel values, is the median value of that pixel neighbourhood’s values.

The median filter should not be confused with the . A median will always be a midpoint value from a sorted value range, whereas a value is equal to the calculated average of a value range. The median filter has the characteristic of reducing image noise whilst still preserving . The mean filter will also reduce image noise, but will do so through generalized , also referred to as , which does not preserve .

Note that when applying a median filter to RGB colour images values need to be determined per individual colour channel.

Frog Filter 3×3 Smoothed

Frog Filter 3x3 Smoothed

Min/Max Edge Detection Algorithm

based in a min/max approach requires relatively few steps, which can be combined in source code implementations to be more efficient from a computational/processing perspective. A higher level logical definition of the steps required can be described as follows:

  1. Image Noise Reduction – If image is required apply a filter to the source image.
  2. Iterate through all of the pixels contained within an .
  3. For each pixel being iterated, determine the neighbouring pixels. The size will be determined by the specified filter size.
  4. Determine the Minimum and Maximum pixel value expressed within the pixel neighbourhood.
  5. Subtract the Minimum from the Maximum value and assign the result to the pixel currently being iterated.
  6. Apply Grayscale conversion to the pixel currently being iterated, only if grayscale conversion had been configured.

Implementing a Min/Max Edge Detection Filter

The source code implementation of the Min/Max Edge Detection Filter declares two methods, a filter method and an method. A median filter and edge detection filter cannot be processed simultaneously. When applying a filter, the median value of a pixel neighbourhood determined from a source image should be expressed in a separate result image. The original source image should not be altered whilst inspecting pixel neighbourhoods and calculating median values. Only once all pixel values in the result image has been set, can the result image serve as a source image to an filter method.

The following code snippet provides the source code definition of the MedianFilter method.

private static byte[] MedianFilter(this byte[] pixelBuffer,
                                    int imageWidth,
                                    int imageHeight,
                                    int filterSize)
{
    byte[] resultBuffer = new byte[pixelBuffer.Length];

    int filterOffset = (filterSize - 1) / 2;
    int calcOffset = 0;
    int stride = imageWidth * pixelByteCount;

    int byteOffset = 0;
    var neighbourCount = filterSize * filterSize;
    int medianIndex = neighbourCount / 2;

    var blueNeighbours = new byte[neighbourCount];
    var greenNeighbours = new byte[neighbourCount];
    var redNeighbours = new byte[neighbourCount];

    for (int offsetY = filterOffset; offsetY <
        imageHeight - filterOffset; offsetY++)
    {
        for (int offsetX = filterOffset; offsetX <
            imageWidth - filterOffset; offsetX++)
        {
            byteOffset = offsetY *
                            stride +
                            offsetX * pixelByteCount;

            for (int filterY = -filterOffset, neighbour = 0;
                filterY <= filterOffset; filterY++)
            {
                for (int filterX = -filterOffset;
                    filterX <= filterOffset; filterX++, neighbour++)
                {
                    calcOffset = byteOffset +
                                    (filterX * pixelByteCount) +
                                    (filterY * stride);

                    blueNeighbours[neighbour] = pixelBuffer[calcOffset];
                    greenNeighbours[neighbour] = pixelBuffer[calcOffset + greenOffset];
                    redNeighbours[neighbour] = pixelBuffer[calcOffset + redOffset];
                }
            }

            Array.Sort(blueNeighbours);
            Array.Sort(greenNeighbours);
            Array.Sort(redNeighbours);

            resultBuffer[byteOffset] = blueNeighbours[medianIndex];
            resultBuffer[byteOffset + greenOffset] = greenNeighbours[medianIndex];
            resultBuffer[byteOffset + redOffset] = redNeighbours[medianIndex];
            resultBuffer[byteOffset + alphaOffset] = maxByteValue;
        }
    }

    return resultBuffer;
}

Notice the definition of three separate arrays, each intended to represent a ’s pixel values related to a specific colour channel. Each neighbourhood colour channel byte array needs to be sorted according to value. The value located at the array index exactly halfway from the start and the end of the array represents the value. When a median value has been determined, the result buffer pixel related to the source buffer pixel in terms of XY Location needs to be set.

Frog Filter 3×3 Smoothed

Frog Filter 3x3 Smoothed

The sample source code defines two overloaded versions of an edge detection method. The first version is defined as an targeting the class. A FilterSize parameter is the only required parameter, intended to specify width/height. In addition, when invoking this method  optional parameters may be specified. When image noise reduction should be implemented the smoothNoise parameter should be defined as true. If resulting images are required in grayscale the last parameter, , should reflect true. The following code snippet provides the definition of the MinMaxEdgeDetection method.

public static Bitmap MinMaxEdgeDetection(this Bitmap sourceBitmap,
                                            int filterSize, 
                                            bool smoothNoise = false, 
                                            bool grayscale = false)
{
    return sourceBitmap.ToPixelBuffer()
                        .MinMaxEdgeDetection(sourceBitmap.Width, 
                                            sourceBitmap.Height, 
                                            filterSize,
                                            smoothNoise,
                                            grayscale)
                        .ToBitmap(sourceBitmap.Width, 
                                    sourceBitmap.Height);
}

The MinMaxEdgeDetection method as expressed above essentially acts as a wrapper method, invoking the overloaded version of this method, performing mapping between objects and byte array pixel buffers.

An overloaded version of the MinMaxEdgeDetection method performs all of the tasks required in through means of minimum maximum value subtraction. The method definition as provided by the following code snippet.

private static byte[] MinMaxEdgeDetection(this byte[] sourceBuffer,
                                          int imageWidth,
                                          int imageHeight,
                                          int filterSize,
                                          bool smoothNoise = false,
                                          bool grayscale = false)
{
    byte[] pixelBuffer = sourceBuffer;

    if (smoothNoise)
    {
        pixelBuffer = sourceBuffer.MedianFilter(imageWidth, 
                                                imageHeight, 
                                                filterSize);
    }

    byte[] resultBuffer = new byte[pixelBuffer.Length];

    int filterOffset = (filterSize - 1) / 2;
    int calcOffset = 0;
    int stride = imageWidth * pixelByteCount;

    int byteOffset = 0;

    byte minBlue = 0, minGreen = 0, minRed = 0;
    byte maxBlue = 0, maxGreen = 0, maxRed = 0;

    for (int offsetY = filterOffset; offsetY <
        imageHeight - filterOffset; offsetY++)
    {
        for (int offsetX = filterOffset; offsetX <
            imageWidth - filterOffset; offsetX++)
        {
            byteOffset = offsetY *
                            stride +
                            offsetX * pixelByteCount;

            minBlue = maxByteValue;
            minGreen = maxByteValue;
            minRed = maxByteValue;

            maxBlue = minByteValue;
            maxGreen = minByteValue;
            maxRed = minByteValue;

            for (int filterY = -filterOffset;
                filterY <= filterOffset; filterY++)
            {
                for (int filterX = -filterOffset;
                    filterX <= filterOffset; filterX++)
                {
                    calcOffset = byteOffset +
                                    (filterX * pixelByteCount) +
                                    (filterY * stride);

                    minBlue = Math.Min(pixelBuffer[calcOffset], minBlue);
                    maxBlue = Math.Max(pixelBuffer[calcOffset], maxBlue);

                    minGreen = Math.Min(pixelBuffer[calcOffset + greenOffset], minGreen);
                    maxGreen = Math.Max(pixelBuffer[calcOffset + greenOffset], maxGreen);

                    minRed = Math.Min(pixelBuffer[calcOffset + redOffset], minRed);
                    maxRed = Math.Max(pixelBuffer[calcOffset + redOffset], maxRed);
                }
            }

            if (grayscale)
            {
                resultBuffer[byteOffset] = ByteVal((maxBlue - minBlue) * 0.114 + 
                                                    (maxGreen - minGreen) * 0.587 + 
                                                    (maxRed - minRed) * 0.299);

                resultBuffer[byteOffset + greenOffset] = resultBuffer[byteOffset];
                resultBuffer[byteOffset + redOffset] = resultBuffer[byteOffset];
                resultBuffer[byteOffset + alphaOffset] = maxByteValue;
            }
            else
            {
                resultBuffer[byteOffset] = (byte)(maxBlue - minBlue);
                resultBuffer[byteOffset + greenOffset] = (byte)(maxGreen - minGreen);
                resultBuffer[byteOffset + redOffset] = (byte)(maxRed - minRed);
                resultBuffer[byteOffset + alphaOffset] = maxByteValue;
            }
        }
    }

    return resultBuffer;
}

As discussed earlier, image if required should be the first task performed. Based on parameter value the method applies a filter to the source image buffer.

When iterating a a comparison is performed between the currently iterated neighbouring pixel’s value and the previously determined minimum and maximum values.

When the grayscale method parameter reflects true, a grayscale algorithm is applied to the difference between the determined maximum and minimum values.

Should the grayscale method parameter reflect false, grayscale algorithm logic will not execute. Instead, the result obtained from subtracting the determined minimum and maximum values are assigned to the relevant pixel and colour channel on the result buffer image.

Frog Filter 3×3 Smoothed

Frog Filter 3x3 Smoothed

Sample Images

This article features several sample images provided as examples. All sample images were created using the sample application. All of the original source images used in generating sample images have been licensed by their respective authors to allow for reproduction here. The following section lists each original source image and related license and copyright details.

Red-eyed Tree Frog (Agalychnis callidryas), photographed near Playa Jaco in Costa Rica © 2007 Careyjamesbalboa (Carey James Balboa) has been released into the public domain by the author.

Red_eyed_tree_frog_edit2

Yellow-Banded Poison Dart Frog © 2013 H. Krisp  is used here under a Creative Commons Attribution 3.0 Unported license.

Bumblebee_Poison_Frog_Dendrobates_leucomelas

Green and Black Poison Dart Frog © 2011 H. Krisp  is used here under a Creative Commons Attribution 3.0 Unported license.

Dendrobates-auratus-goldbaumsteiger

Atelopus certus calling male © 2010 Brian Gratwicke  is used here under a Creative Commons Attribution 2.0 Generic license.

Atelopus_certus_calling_male_edit

Tyler’s Tree Frog (Litoria tyleri) © 2006 LiquidGhoul  has been released into the public domain by the author.

Litoria_tyleri

Dendropsophus microcephalus © 2010 Brian Gratwicke  is used here under a Creative Commons Attribution 2.0 Generic license.

Dendropsophus_microcephalus_-_calling_male_(Cope,_1886)

Related Articles and Feedback

Feedback and questions are always encouraged. If you know of an alternative implementation or have ideas on a more efficient implementation please share in the comments section.

I’ve published a number of articles related to imaging and images of which you can find URL links here:

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C# How to: Standard Deviation Edge Detection

Article Purpose

This article explores detection implemented through computing neighbourhood on RGB  . The main sections of this article consists of a detailed explanation of the concepts related to the standard deviation edge detection algorithm and an in-depth discussion and a practical implementation through source code.

Butterfly Filter 3×3 Factor 5.0

Butterfly Filter 3x3 Factor 5.0

Sample Source Code

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

Using the Sample Application

This article’s accompanying sample source code includes a based sample application. The sample application provides an implementation of the concepts explored by this article. Concepts discussed can be easily replicated and tested by using the sample application.

Source/input files can be specified from the local system when clicking the Load Image button. Additionally users also have the option to save resulting filtered by clicking the Save Image button.

The sample application user interface exposes three filter configuration values to the end user in the form of predefined filter size values, a output flag and a factor. End users can configure whether filtered result images should express using source colour values or in . The filter size value specified by the user determines the number of included when calculating values.

Filter size has a direct correlation to the extend at which gradient edges will be represented in resulting images. Faint edge values require larger filter size values in order to be expressed in a resulting output image. Larger filter size values require additional computation and would thus have a longer completion time when compared to smaller filter size values.

The following screenshot captures the Standard Deviation Edge Detection sample application in action.

Standard Deviation Edge Detection Screenshot

Standard Deviation

can be achieved through a variety of methods, each associated with particular benefits and trade offs. This article is focussed on through implementing calculations on a neighbourhood.

Pixel Neighbourhood

A pixel neighbourhood refers to a set of pixels, all of which are related through location coordinates. The width and height of a pixel neighbourhood must be equal, in other words, a pixel neighbourhood can only be square. Additionally, the width/height of a pixel neighbourhood must be an uneven value. When inspecting a pixel’s neighbouring pixels, the pixel being inspected will always be located at the exact center of the pixel neighbourhood. Only when a pixel neighbourhood’s width/height are an uneven value can such a pixel neighbourhood have an exact center pixel. Each pixel represented in an has a different set of neighbours, some neighbours overlap, but no two pixels have the exact same neighbours. A pixel’s neighbouring pixels can be determined when considering the pixel to be at the center of a block of pixels, extending half the neighbourhood size less one in horizontal, vertical and diagonal directions.

Butterfly Filter 3×3 Factor 5

Butterfly Filter 3x3 Factor 5

Pixel Neighbourhood Mean value

value calculation forms a core part in calculating . The mean value from a set of values could be considered equivalent to the value set’s average value. The average of a set of  values can be calculated as the sum total of all the values in a set, divided by the number of values in the set.

Standard Deviation

From the page we gain the following quote:

In statistics, the standard deviation (SD, also represented by the Greek letter sigma, σ for the population standard deviation or s for the sample standard deviation) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. A standard deviation close to 0 indicates that the data points tend to be very close to the (also called the expected value) of the set, while a high standard deviation indicates that the data points are spread out over a wider range of values

A pixel neighbourhood’s can indicate whether a significant change in image gradient is present in a neighbourhood. A large value is an indication that the neighbourhood’s pixel values could be spread far from the calculated . Inversely, a small will indicate that the neighbourhood’s pixel values are closer to the calculated . A sudden change in image gradient will equate to a large standard deviation.

Steps required in calculating can be described as follows:

  1. Calculate the Mean value. Calculate the sum of all pixels in a pixel neighbourhood then divide the sum total using the number of pixels contained in a neighbourhood. In essence calculating the mean value should be seen as calculating the average of all the pixels in a neighbourhood.
  2. Calculate combined Variance using value. Subtract the mean value from each pixel in the neighbourhood, the result should be squared and added to a sum total. should then be calculated as the calculated mean subtracted squared pixel value divided using the number of pixels in a neighbourhood.
  3. Calculate as the Variance square root.

Butterfly Filter 3×3 Factor 5

Butterfly Filter 3x3 Factor 5

Standard Deviation Edge Detection Algorithm

The standard deviation edge detection algorithm is based in the concept of , providing additional capabilities. The algorithm allows for a more prominent expression of through means of a  variance factor. Calculated values can be increased or decreased when implementing a variance factor. When variances are less significant, resulting images will express gradient edges at faint/low intensity levels. Providing a factor will result in output images expressing gradient edges at a higher intensity.

factor and filter size should not be confused. When source gradient edges are expressed at low intensities, higher filter sizes would result in those low intensity source edges to be expressed in resulting images. In a scenario where high intensity gradient edges from a source are expressed in resulting images at low intensities, a higher factor would increase resulting edge intensity.

The following list provides a summary of the steps required  to implement the algorithm:

  1. Iterate through all of the pixels contained within an .
  2. For each pixel being iterated, determine the neighbouring pixels. The pixel neighbourhood size will be determined by the specified filter size.
  3. Calculate the Mean value of the current pixel neighbourhood.
  4. Calculate the Variance. Subtract the value from each neighbourhood pixel, the result should be squared and summed to a total value. Finally, the variance total value should be divided by the number of pixels that make up the pixel  neighbourhood. If a variance factor had been specified, the calculated variance value should be multiplied against it and the result assigned as the new calculated value.
  5. Calculate the Standard Deviation. Once the has been calculated the can be expressed as the square root of the calculated value. The value should be assigned to the result buffer pixel relating to the source buffer pixel currently being iterated.

It is important to note that the steps as described above should be applied per individual colour channel, Red, Green and Blue.

Butterfly Filter 3×3 Factor 4.5

Butterfly Filter 3x3 Factor 4.5

Implementing a Standard Deviation Edge Detection filter

The sample source code that accompanies this article provides a public targeting the class. A private overloaded implementation of the StandardDeviationEdgeDetection method performs the bulk of the required functionality. The following code snippet illustrates the public overloaded version of the StandardDeviationEdgeDetection method:

public static Bitmap StandardDeviationEdgeDetection(this Bitmap sourceBuffer, 
                                                    int filterSize, 
                                                    float varianceFactor = 1.0f, 
                                                    bool grayscaleOutput = true)
{
     return sourceBuffer.ToPixelBuffer()
                        .StandardDeviationEdgeDetection(sourceBuffer.Width, 
                                                        sourceBuffer.Height,
                                                        filterSize,
                                                        varianceFactor,
                                                        grayscaleOutput)
                         .ToBitmap(sourceBuffer.Width, sourceBuffer.Height);
}

The StandardDeviationEdgeDetection method accepts 3 parameters, the first parameter serves to signal that the method is an targeting the class. A brief description of the other parameters as follows:

  • filterSize determines the pixel neighbourhood size. Note that the parameter is expected to reflect the pixel neighbourhood width/height. As an example, a filterSize  parameter value provided as 3 would equate to a pixel neighbourhood consisting of 9 pixels, as would a filterSize of 5 indicate a neighbourhood of 25 pixels.
  • varianceFactor signifies the factor value applied to a calculated variance.
  • grayscale being a boolean value indicates whether the resulting should be represented in , or in the original colour values from the source .

Butterfly Filter 3×3 Factor 4 

Butterfly Filter 3x3 Factor 4

The following code snippet relates the private implementation of the StandardDeviationEdgeDetection method, which performs all of the tasks required to implement the standard deviation edge detection algorithm.

private static byte[] StandardDeviationEdgeDetection(this byte[] pixelBuffer, 
                                                     int imageWidth, 
                                                     int imageHeight,
                                                     int filterSize,
                                                     float varianceFactor = 1.0f,
                                                     bool grayscaleOutput = true)
{
    byte[] resultBuffer = new byte[pixelBuffer.Length];

    int filterOffset = (filterSize - 1) / 2;
    int calcOffset = 0;
    int stride = imageWidth * pixelByteCount;
            
    int byteOffset = 0;
    var neighbourCount = filterSize * filterSize;
            
    var blueNeighbours = new int[neighbourCount];
    var greenNeighbours = new int[neighbourCount];
    var redNeighbours = new int[neighbourCount];

    double resetValue = 0;
    double meanBlue = 0, meanGreen = 0, meanRed = 0;
    double varianceBlue = 0, varianceGreen = 0, varianceRed = 0;

    varianceFactor = varianceFactor * varianceFactor;

    for (int offsetY = filterOffset; offsetY <
        imageHeight - filterOffset; offsetY++)
    {
        for (int offsetX = filterOffset; offsetX <
            imageWidth - filterOffset; offsetX++)
        {
            byteOffset = offsetY *
                            stride +
                            offsetX * pixelByteCount;

            meanBlue = resetValue;
            meanGreen = resetValue;
            meanRed = resetValue;

            varianceBlue = resetValue;
            varianceGreen = resetValue;
            varianceRed = resetValue;

            for (int filterY = -filterOffset, neighbour = 0;
                filterY <= filterOffset; filterY++)
            {
                for (int filterX = -filterOffset;
                    filterX <= filterOffset; filterX++, neighbour++)
                {
                    calcOffset = byteOffset +
                                    (filterX * pixelByteCount) +
                                    (filterY * stride);

                    blueNeighbours[neighbour] = pixelBuffer[calcOffset];
                    greenNeighbours[neighbour] = pixelBuffer[calcOffset + 1];
                    redNeighbours[neighbour] = pixelBuffer[calcOffset + 2];
                }
            }

            meanBlue = blueNeighbours.Average();
            meanGreen = greenNeighbours.Average();
            meanRed = redNeighbours.Average();

            for (int n = 0; n < neighbourCount; n++)
            {
                varianceBlue = varianceBlue + 
                                SquareNumber(blueNeighbours[n] - meanBlue);
                varianceGreen = varianceGreen + 
                                SquareNumber(greenNeighbours[n] - meanGreen);
                varianceRed = varianceRed + 
                                SquareNumber(redNeighbours[n] - meanRed);
            }

            varianceBlue = varianceBlue / 
                            neighbourCount * 
                            varianceFactor;

            varianceGreen = varianceGreen /
                            neighbourCount * 
                            varianceFactor;

            varianceRed = varianceRed / 
                            neighbourCount * 
                            varianceFactor;

            if (grayscaleOutput)
            {
                var pixelValue = ByteVal(ByteVal(Math.Sqrt(varianceBlue)) |
                                         ByteVal(Math.Sqrt(varianceGreen)) | 
                                         ByteVal(Math.Sqrt(varianceRed)));

                resultBuffer[byteOffset] = pixelValue;
                resultBuffer[byteOffset + 1] = pixelValue;
                resultBuffer[byteOffset + 2] = pixelValue;
                resultBuffer[byteOffset + 3] = Byte.MaxValue;
            }
            else
            {
                resultBuffer[byteOffset] = ByteVal(Math.Sqrt(varianceBlue));
                resultBuffer[byteOffset + 1] = ByteVal(Math.Sqrt(varianceGreen));
                resultBuffer[byteOffset + 2] = ByteVal(Math.Sqrt(varianceRed));
                resultBuffer[byteOffset + 3] = Byte.MaxValue;
            }
        }
    }

    return resultBuffer;
}

This method features several for loops, resulting in each pixel being iterated. Notice how the two inner most loops declare negative initializer values. In order to determine a pixel’s neighbourhood, the pixel should be considered as being located at the exact center of the neighbourhood. Negative initializer values enable the code to determine neighbouring pixels located to the left and above of the pixel being iterated.

A pixel neighbourhood needs to be determined in terms of each colour channel, Red, Green and Blue. The pixel neighbourhood of each colour channel must be averaged individually.  Logically it follows that pixel neighbourhood should also be calculated per colour channel.

The method signature indicates the varianceFactor parameter should be optional and assigned a default value of 1.0. Should a variance factor not be required, implementing a default factor value of 1.0 will not result in any change to the calculated value.

Butterfly Filter 3x3 Factor 4

When output has been configured the resulting output pixel will express the same value on all three colour channels. The value will be calculated through the application of a bitwise OR operation, applied to the of each colour channel. The square root of a pixel neighbourhood’s provides the value for that pixel neighbourhood.

If output had not been configured the resulting pixel colour channels will be assigned the of the related colour channel on the source pixel.

private const byte maxByteValue = Byte.MaxValue;
private const byte minByteValue = Byte.MinValue;

public static byte ByteVal(int val)
{
    if (val < minByteValue) { return  minByteValue; }
    else if (val > maxByteValue) { return  maxByteValue; }
    else { return (byte)val; }
}

The StandardDeviationEdgeDetection method reflects several references to the ByteVal method, as illustrated in the code snippet above. Casting double and int values to values could result in values exceeding the upper and lower bounds allowed by the type. The ByteVal method tests whether a value would exceed upper and lower bounds, when determined to do so the resulting value is assigned either the upper inclusive bound or lower inclusive bound value, depending on the bound being exceeded.

Bee Filter 3×3 Factor 5

Bee Filter 3x3 Factor 5

Sample Images

This article features several sample images provided as examples. All sample images were created using the sample application. All of the original source images used in generating sample images have been licensed by their respective authors to allow for reproduction here. The following section lists each original source image and related license and copyright details.

Viceroy (Limenitis archippus), Mer Bleue Conservation Area, Ottawa, Ontario © 2008 D. Gordon E. Robertson is used here under a Creative Commons Attribution-Share Alike 3.0 Unported license.

Viceroy (Limenitis archippus), Mer Bleue Conservation Area, Ottawa, Ontario


Old World Swallowtail on Buddleja davidii © 2008 Thomas Bresson is used here under a Creative Commons Attribution 2.0 Generic license.Old World Swallowtail on Buddleja davidii


Cethosia cyane butterfly © 2006 Airbete is used here under a Creative Commons Attribution-Share Alike 3.0 Unported license.

Cethosia_cyane


“Weiße Baumnymphe (Idea leuconoe) fotografiert im Schmetterlingshaus des Maximilianpark Hamm”  © 2009 Steffen Flor is used here under a  Creative Commons Attribution-Share Alike 3.0 Unported license.

Weiße Baumnymphe (Idea leuconoe) fotografiert im Schmetterlingshaus des Maximilianpark Hamm


"Dark Blue Tiger tirumala septentrionis by kadavoor" © 2010 Jeevan Jose, Kerala, India is used here under a Creative Commons Attribution-ShareAlike 4.0 International License

Dark Blue Tiger tirumala septentrionis by kadavoor


"Common Lime Butterfly Papilio demoleus by Kadavoor" © 2010 Jeevan Jose, Kerala, India is used here under a Creative Commons Attribution-ShareAlike 4.0 International License

Common Lime Butterfly Papilio demoleus by Kadavoor


Syrphidae, Knüllwald, Hessen, Deutschland © 2007 Fritz Geller-Grimm is used here under a Creative Commons Attribution-Share Alike 3.0 Unported license

Syrphidae, Knüllwald, Hessen, Deutschland


Related Articles and Feedback

Feedback and questions are always encouraged. If you know of an alternative implementation or have ideas on a more efficient implementation please share in the comments section.

I’ve published a number of articles related to imaging and images of which you can find URL links here:

C# How to: Image Distortion Blur

Article Purpose

This article explores the process of implementing an Image Distortion Blur filter. This image filter is classified as a non-photo realistic image filter, primarily implemented in rendering artistic effects.

Flower: Distortion Factor 15

Flower: Distortion Factor 15

Sample Source Code

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

Flower: Distortion Factor 10

Flower: Distortion Factor 10

Using the Sample Application

The sample source code that accompanies this article includes a based sample application. The concepts explored in this article have all been implemented as part of the sample application. From an end user perspective the following configurable options are available:

  • Load/Save Images – Clicking the Load Image button allows a user to specify a source/input . If desired, output filtered can be saved to the local system by clicking the Save Image button.
  • Distortion Factor – The level or intensity of distortion applied when implementing the filter can be specified when adjusting the Distortion Factor through the user interface. Lower factor values result in less distortion being evident in resulting . Specifying higher factor values result in more intense distortion being applied.

The following image is screenshot of the Image Distortion Blur sample application:

ImageDistortionBlur_SampleApplication

Flower: Distortion Factor 10

Flower: Distortion Factor 10

Flower: Distortion Factor 10

Flower: Distortion Factor 10

Image Distortion

In this article and the accompanying sample source code are distorted through slightly adjusting each individual ’s coordinates. The direction and distance by which coordinates are adjusted differ per as a result of being randomly selected. The maximum distance offset applied depends on the user specified Distortion Factor. Once all coordinates have been updated, implementing a provides smoothing and an effect.

Applying an Image Distortion Filter requires implementing the following steps:

  1. Iterate Pixels – Each forming part of the source/input should be iterated.
  2. Calculate new Coordinates – For every being iterated generate two random values representing XY-coordinate offsets to be applied to a ’s current coordinates. Offset values can equate to less than zero in order to represent coordinates above or to the left of the current .
  3. Apply Median Filter – The newly offset will appear somewhat speckled in the resulting . Applying a reduces the speckled appearance whilst retaining a distortion effect.

Flower: Distortion Factor 10

Flower: Distortion Factor 10

Flower: Distortion Factor 10

Flower: Distortion Factor 10

Median Filter

Applying a is the final step required when implementing an Image Distortion Blur filter. are often implemented in reducing . The method of image distortion illustrated in this article express similarities when compared to . In order to soften the appearance of we implement a .

A can be applied through implementing the following steps:

  1. Iterate Pixels – Each forming part of the source/input should be iterated.
  2. Inspect Pixel Neighbourhood – Each neighbouring in relation to the currently being iterated should be added to a temporary collection.
  3. Determine Neighbourhood Median – Once all neighbourhood have been added to a temporary collection, sort the collection by value. The element value located at the middle of the collection represents the neighbourhood’s value.

Flower: Distortion Factor 10

Flower: Distortion Factor 10

Flower: Distortion Factor 15

Flower: Distortion Factor 15

Implementing Image Distortion

The sample source code defines the DistortionBlurFilter method, an targeting the class. The following code snippet illustrates the implementation:

public static Bitmap DistortionBlurFilter( 
         this Bitmap sourceBitmap, int distortFactor) 
{
    byte[] pixelBuffer = sourceBitmap.GetByteArray(); 
    byte[] resultBuffer = sourceBitmap.GetByteArray(); 

int imageStride = sourceBitmap.Width * 4; int calcOffset = 0, filterY = 0, filterX = 0; int factorMax = (distortFactor + 1) * 2; Random rand = new Random();
for (int k = 0; k + 4 < pixelBuffer.Length; k += 4) { filterY = distortFactor - rand.Next(0, factorMax); filterX = distortFactor - rand.Next(0, factorMax);
if (filterX * 4 + (k % imageStride) < imageStride && filterX * 4 + (k % imageStride) > 0) { calcOffset = k + filterY * imageStride + 4 * filterX;
if (calcOffset >= 0 && calcOffset + 4 < resultBuffer.Length) { resultBuffer[calcOffset] = pixelBuffer[k]; resultBuffer[calcOffset + 1] = pixelBuffer[k + 1]; resultBuffer[calcOffset + 2] = pixelBuffer[k + 2]; } } }
return resultBuffer.GetImage(sourceBitmap.Width, sourceBitmap.Height).MedianFilter(3); }

Flower: Distortion Factor 15

Flower: Distortion Factor 15

Implementing a Median Filter

The MedianFilter targets the class. The implementation as follows:

public static Bitmap MedianFilter(this Bitmap sourceBitmap, 
                                  int matrixSize) 
{ 
    byte[] pixelBuffer = sourceBitmap.GetByteArray(); 
    byte[] resultBuffer = new byte[pixelBuffer.Length]; 
    byte[] middlePixel; 

int imageStride = sourceBitmap.Width * 4; int filterOffset = (matrixSize - 1) / 2; int calcOffset = 0, filterY = 0, filterX = 0; List<int> neighbourPixels = new List<int>();
for (int k = 0; k + 4 < pixelBuffer.Length; k += 4) { filterY = -filterOffset; filterX = -filterOffset; neighbourPixels.Clear();
while (filterY <= filterOffset) { calcOffset = k + (filterX * 4) + (filterY * imageStride);
if (calcOffset > 0 && calcOffset + 4 < pixelBuffer.Length) { neighbourPixels.Add(BitConverter.ToInt32( pixelBuffer, calcOffset)); }
filterX++;
if (filterX > filterOffset) { filterX = -filterOffset; filterY++; } }
neighbourPixels.Sort(); middlePixel = BitConverter.GetBytes( neighbourPixels[filterOffset]);
resultBuffer[k] = middlePixel[0]; resultBuffer[k + 1] = middlePixel[1]; resultBuffer[k + 2] = middlePixel[2]; resultBuffer[k + 3] = middlePixel[3]; }
return resultBuffer.GetImage(sourceBitmap.Width, sourceBitmap.Height); }

Flower: Distortion Factor 25

Flower: Distortion Factor 25

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:

674px-Lil_chalcedonicum_01EB_Griechenland_Hrisomiglia_17_07_01

683px-Lil_carniolicum_subsp_ponticum_01EB_Tuerkei_Ikizdere_02_07_93

1022px-LiliumSargentiae

1024px-Lilium_longiflorum_(Easter_Lily)

1280px-LiliumBulbiferumCroceumBologna

LiliumSuperbum1

Orange_Lilium_-_Relic38_-_Ontario_Canada

White_and_yellow_flower

Related Articles and Feedback

Feedback and questions are always encouraged. If you know of an alternative implementation or have ideas on a more efficient implementation please share in the comments section.

I’ve published a number of articles related to imaging and images of which you can find URL links here:

C# How to: Image Blur

Article Purpose

This article serves to provides an introduction and discussion relating to methods and techniques. The Image Blur methods covered in this article include: , , , and  .

Daisy: Mean 9×9

Daisy Mean 9x9

Sample Source Code

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

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 based application of which the user interface enables the user to select an type to implement.

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

Daisy: Mean 7×7

Daisy Mean 7x7

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

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

Image Blur Filter Sample Application

Image Blur Overview

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

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

In this article and the accompanying sample source code all methods of supported have been implemented through , with the exception of the filter. Each of the supported methods in essence only represent a different   . The technique capable of achieving optimal results will to varying degrees be dependent on the features present in the specified source/input . 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

Daisy Mean 9x9

Mean Filter/Box Blur

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

A box blur is an 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 .

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.

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

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

Mean Filter Blur 5x5 Kernel

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

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

Daisy Mean 5×5

Daisy Mean 5x5

Gaussian Blur

The method of is a popular and often implemented filter. In contrast to the method produce resulting appearing to contain a more uniform level of smoothing. When implementing a is often applied to source/input resulting in . The has a good level of edge preservation, hence being used in operations.

From we gain the following :

A Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a . 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 results from the filter being computationally intensive. The following represents a 5×5 . The sum total of all elements in the equate to 159, therefore a factor value of 1.0 / 159.0 will be implemented.

Guassian Blur 5x5 Kernel

Daisy: Gaussian 5×5

Daisy Gaussian 5x5

Median Filter Blur

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

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

Daisy: Median 7×7

Daisy Median 7x7

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

  1. Iterate each pixel that forms part of the source/input .
  2. 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.
  3. 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.
  4. The pixel value located at the middle of the sorted neighbourhood array qualifies as the value. The newly determined value should be assigned to the pixel currently being iterated.
  5. Repeat the steps listed above until all pixels within the source/input have been iterated.

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

Daisy: Median 9×9

Daisy Median 9x9

Motion Blur

The sample source implements filters. in the traditional sense has been association with photography and video capturing. 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 changing  before the frame being captured has completed.

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

Daisy: Motion Blur 7×7 135 Degrees

Daisy Motion Blur 7x7 135 Degrees

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

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

MotionBlur5x5

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 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 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

Daisy Motion Blur 9x9

The Matrix class serves as a collection of  various 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

Daisy Median 7x7

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

The definition of the MedianFilter 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

Daisy Motion Blur 9x9

The sample source code performs by invoking the ConvolutionFilter .

The definition of the ConvolutionFilter 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

Purple_osteospermum

Daisy: Gaussian 3×3

Daisy Gaussian 3x3

Daisy: Gaussian 5×5

Daisy Gaussian 5x5

Daisy: Mean 3×3

Daisy Mean 3x3

Daisy: Mean 5×5

Daisy Mean 5x5

Daisy: Mean 7×7

Daisy Mean 7x7

Daisy: Mean 9×9

Daisy Mean 9x9

Daisy: Median 3×3

Daisy Median 3x3

Daisy: Median 5×5

Daisy Median 5x5

Daisy: Median 7×7

Daisy Median 7x7

Daisy: Median 9×9

Daisy Median 9x9

Daisy: Median 11×11

Daisy Median 11x11

Daisy: Motion Blur 5×5

Daisy Motion Blur 5x5

Daisy: Motion Blur 5×5 45 Degrees

Daisy Motion Blur 5x5 45 Degrees

Daisy: Motion Blur 5×5 135 Degrees

Daisy Motion Blur 5x5 135 Degrees

Daisy: Motion Blur 7×7

Daisy Motion Blur 7x7

Daisy: Motion Blur 7×7 45 Degrees

Daisy Motion Blur 7x7 45 Degree

Daisy: Motion Blur 7×7 135 Degrees

Daisy Motion Blur 7x7 135 Degrees

Daisy: Motion Blur 9×9

Daisy Motion Blur 9x9

Daisy: Motion Blur 9×9 45 Degrees

Daisy Motion Blur 9x9 45 Degrees

Daisy: Motion Blur 9×9 135 Degrees

Daisy Motion Blur 9x9 135 Degrees

Related Articles and Feedback

Feedback and questions are always encouraged. If you know of an alternative implementation or have ideas on a more efficient implementation please share in the comments section.

I’ve published a number of articles related to imaging and images of which you can find URL links here:

C# How to: Image Median Filter

Article purpose

The objective of this article is focussed on providing a discussion on implementing a on an . 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 .

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:

Image Median Filter Sample Application

What is a Median Filter

From we gain the following :

In , 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 technique, often used to remove noise. Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, on an image). Median filtering is very widely used in digital 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 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 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 for more details.

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

Applying a Median Filter

The sample source code defines the MedianFilter targeting the class. The matrixSize parameter determines the intensity of the being applied.

The MedianFilter iterates each pixel of the source . When iterating 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 , represented as an array of pixel colour component .

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 Viatourwww.Lucnix.be and can be downloaded from Wikipedia.

The Original Source Image

Ara_ararauna_Luc_Viatour

Median 3×3 Filter

Median Filter 3x3

Median 5×5 Filter

Median Filter 5x5

Median 7×7 Filter

Median Filter 7x7

Median 9×9 Filter

Median Filter 9x9

Median 11×11 Filter

Median Filter 11x11

Median 13×13 Filter

Median Filter 13x13

Related Articles and Feedback

Feedback and questions are always encouraged. If you know of an alternative implementation or have ideas on a more efficient implementation please share in the comments section.

I’ve published a number of articles related to imaging and images of which you can find URL links here:


Dewald Esterhuizen

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