C# How to: Fuzzy Blur Filter

Article Purpose

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

Frog: Filter Size 19×19

Frog: Filter Size 19x19

Sample Source Code

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

Using the Sample Application

The sample source code accompanying this article includes a based test application. The concepts explored throughout this article can be replicated/tested using the sample application.

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

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

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

Fuzzy Blur Filter Sample Application

Frog: Filter Size 9×9

Frog: Filter Size 9x9

Fuzzy Blur Overview

The Fuzzy Blur Filter relies on the interference of when performing in order to create a fuzzy effect. In addition results from performing a .

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

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

Frog: Filter Size 9×9

Frog: Filter Size 9x9

Mean Filter

A Blur, also known as a , can be performed through . The size of the / implemented when preforming will be determined through user input.

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

Mean Kernel

An alternative expression can also be:

Mean Kernel

Frog: Filter Size 9×9

Frog: Filter Size 9x9

Boolean Edge Detection without a local threshold

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

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

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

Note: A detailed article on Boolean Edge detection implementing a local threshold can be found here:

Frog: Filter Size 9×9

Frog: Filter Size 9x9

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

Boolean Edge Masks

Frog: Filter Size 13×13

Frog: Filter Size 13x13

Implementing a Mean Filter

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

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

double blue = 0.0, green = 0.0, red = 0.0; double factor = 1.0 / (meanSize * meanSize);
int imageStride = sourceBitmap.Width * 4; int filterOffset = meanSize / 2; int calcOffset = 0, filterY = 0, filterX = 0;
for (int k = 0; k + 4 < pixelBuffer.Length; k += 4) { blue = 0; green = 0; red = 0; filterY = -filterOffset; filterX = -filterOffset;
while (filterY <= filterOffset) { calcOffset = k + (filterX * 4) + (filterY * imageStride);
calcOffset = (calcOffset < 0 ? 0 : (calcOffset >= pixelBuffer.Length - 2 ? pixelBuffer.Length - 3 : calcOffset));
blue += pixelBuffer[calcOffset]; green += pixelBuffer[calcOffset + 1]; red += pixelBuffer[calcOffset + 2];
filterX++;
if (filterX > filterOffset) { filterX = -filterOffset; filterY++; } }
resultBuffer[k] = ClipByte(factor * blue); resultBuffer[k + 1] = ClipByte(factor * green); resultBuffer[k + 2] = ClipByte(factor * red); resultBuffer[k + 3] = 255; }
return resultBuffer.GetImage(sourceBitmap.Width, sourceBitmap.Height); }

Frog: Filter Size 19×19

Frog: Filter Size 19x19

Implementing Boolean Edge Detection

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

The following code snippet provides the definition of the BooleanEdgeDetectionFilter :

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

List<string> edgeMasks = GetBooleanEdgeMasks(); int imageStride = sourceBitmap.Width * 4; int matrixMean = 0, pixelTotal = 0; int filterY = 0, filterX = 0, calcOffset = 0; string matrixPatern = String.Empty;
for (int k = 0; k + 4 < pixelBuffer.Length; k += 4) { matrixPatern = String.Empty; matrixMean = 0; pixelTotal = 0; filterY = -1; filterX = -1;
while (filterY < 2) { calcOffset = k + (filterX * 4) + (filterY * imageStride);
calcOffset = (calcOffset < 0 ? 0 : (calcOffset >= pixelBuffer.Length - 2 ? pixelBuffer.Length - 3 : calcOffset)); matrixMean += pixelBuffer[calcOffset]; matrixMean += pixelBuffer[calcOffset + 1]; matrixMean += pixelBuffer[calcOffset + 2];
filterX += 1;
if (filterX > 1) { filterX = -1; filterY += 1; } }
matrixMean = matrixMean / 9; filterY = -1; filterX = -1;
while (filterY < 2) { calcOffset = k + (filterX * 4) + (filterY * imageStride);
calcOffset = (calcOffset < 0 ? 0 : (calcOffset >= pixelBuffer.Length - 2 ? pixelBuffer.Length - 3 : calcOffset));
pixelTotal = pixelBuffer[calcOffset]; pixelTotal += pixelBuffer[calcOffset + 1]; pixelTotal += pixelBuffer[calcOffset + 2]; matrixPatern += (pixelTotal > matrixMean ? "1" : "0"); filterX += 1;
if (filterX > 1) { filterX = -1; filterY += 1; } }
if (edgeMasks.Contains(matrixPatern)) { resultBuffer[k] = ClipByte(resultBuffer[k] * edgeFactor);
resultBuffer[k + 1] = ClipByte(resultBuffer[k + 1] * edgeFactor);
resultBuffer[k + 2] = ClipByte(resultBuffer[k + 2] * edgeFactor); } }
return resultBuffer.GetImage(sourceBitmap.Width, sourceBitmap.Height); }

Frog: Filter Size 13×13

Frog: Filter Size 13x13

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

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

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

Frog: Filter Size 19×19

Frog: Filter Size 19x19

Implementing a Fuzzy Blur Filter

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

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

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

Frog: Filter Size 3×3

Frog: Filter Size 3x3

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:

Litoria_tyleri

Schrecklicherpfeilgiftfrosch-01

Dendropsophus_microcephalus_-_calling_male_(Cope,_1886)

Atelopus_zeteki1

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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about.me :: Dewald Esterhuizen

Dewald Esterhuizen

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