[Restored title] Fuzzy impulse detection in grayscale image

Question

Jo 2012-11-19

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/54186-restored-title-fuzzy-impulse-detection-in-grayscale-image

评论： jerin 2014-7-1

[Restored question]

Hello there,

I need help please. I want a matlab code that detect salt & pepper noise in grayscale image using Fuzzy technique. Then apply median filter to all noisy pixels.

appreciate your help.

Anwar

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Jan 2012-12-8

编辑：Jan 2012-12-8

@Jo: Why did you delete the contents of the question and your comments? The nature of this forum is to share solutions. Removing the question after Image Analyst has posted a large answer is not friendly.

The only cause I can image for deleting a question is that somebody wants to submit a solution copied from this forum as his/her own work, e.g. for a homework. This would be a misuse of the forum and because I do not want cheating to be supported, I've restored the original text question. Btw., the original text is stored in Google's cache, such that removing it in the forum is not effective at all.

jerin 2014-7-1

Thanks a lot for median filter. Could you provide fuzzy based one please? It would be very helpful.

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Answer 1

Image Analyst 2012-11-19

2
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/54186-restored-title-fuzzy-impulse-detection-in-grayscale-image#answer_65859

在 MATLAB Online 中打开

clc;  % Clear command window.
clear;  % Delete all variables.
close all;  % Close all figure windows except those created by imtool.
imtool close all;  % Close all figure windows created by imtool.
workspace;  % Make sure the workspace panel is showing.
fontSize = 15;
% Read in a standard MATLAB color demo image.
folder = fullfile(matlabroot, '\toolbox\images\imdemos');
baseFileName = 'peppers.png';
fullFileName = fullfile(folder, baseFileName);
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
if ~exist(fullFileName, 'file')
  % Didn't find it there.  Check the search path for it.
  fullFileName = baseFileName; % No path this time.
  if ~exist(fullFileName, 'file')
    % Still didn't find it.  Alert user.
    errorMessage = sprintf('Error: %s does not exist.', fullFileName);
    uiwait(warndlg(errorMessage));
    return;
  end
end
rgbImage = imread(fullFileName);
% Get the dimensions of the image.  numberOfColorBands should be = 3.
[rows columns numberOfColorBands] = size(rgbImage);
% Display the original color image.
subplot(3, 4, 1);
imshow(rgbImage);
title('Original color Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'Position', get(0,'Screensize')); 
% Extract the individual red, green, and blue color channels.
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
% Display the individual red, green, and blue color channels.
subplot(3, 4, 2);
imshow(redChannel);
title('Red Channel', 'FontSize', fontSize);
subplot(3, 4, 3);
imshow(greenChannel);
title('Green Channel', 'FontSize', fontSize);
subplot(3, 4, 4);
imshow(blueChannel);
title('Blue Channel', 'FontSize', fontSize);
% Generate a noisy image.  This has salt and pepper noise independently on
% each color channel so the noise may be colored.
noisyRGB = imnoise(rgbImage,'salt & pepper', 0.05);
subplot(3, 4, 5);
imshow(noisyRGB);
title('Image with Salt and Pepper Noise', 'FontSize', fontSize);
% Extract the individual red, green, and blue color channels.
redChannel = noisyRGB(:, :, 1);
greenChannel = noisyRGB(:, :, 2);
blueChannel = noisyRGB(:, :, 3);
% Display the noisy channel images.
subplot(3, 4, 6);
imshow(redChannel);
title('Noisy Red Channel', 'FontSize', fontSize);
subplot(3, 4, 7);
imshow(greenChannel);
title('Noisy Green Channel', 'FontSize', fontSize);
subplot(3, 4, 8);
imshow(blueChannel);
title('Noisy Blue Channel', 'FontSize', fontSize);
% Median Filter the channels:
redMF = medfilt2(redChannel, [3 3]);
greenMF = medfilt2(greenChannel, [3 3]);
blueMF = medfilt2(blueChannel, [3 3]);
% Find the noise in the red.
noiseImage = (redChannel == 0 | redChannel == 255);
% Get rid of the noise in the red by replacing with median.
noiseFreeRed = redChannel;
noiseFreeRed(noiseImage) = redMF(noiseImage);
% Find the noise in the green.
noiseImage = (greenChannel == 0 | greenChannel == 255);
% Get rid of the noise in the green by replacing with median.
noiseFreeGreen = greenChannel;
noiseFreeGreen(noiseImage) = greenMF(noiseImage);
% Find the noise in the blue.
noiseImage = (blueChannel == 0 | blueChannel == 255);
% Get rid of the noise in the blue by replacing with median.
noiseFreeBlue = blueChannel;
noiseFreeBlue(noiseImage) = blueMF(noiseImage);
% Reconstruct the noise free RGB image
rgbFixed = cat(3, noiseFreeRed, noiseFreeGreen, noiseFreeBlue);
subplot(3, 4, 9);
imshow(rgbFixed);
title('Restored Image', 'FontSize', fontSize);

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Image Analyst 2012-11-19

编辑：Jan 2012-12-8

在 MATLAB Online 中打开

[Restored former comment, Jan] Jo has written:

But these for color image. What about if it is grayscale image?

[Image Analyst's answer]:

Make the obvious modifications. If you can't get it, I'll help you out later tonight, but I have to leave now.

Image Analyst 2012-11-20

编辑：Image Analyst 2012-11-20

在 MATLAB Online 中打开

OK, maybe it's a lot tougher than I thought. Here's the gray scale version. The real part of the algorithm is simply 4 lines: a call to medfilt2(), finding the noise, initializing an output image, and replacing the pixels. Everything else is just making it a fancy demo.

clc;  % Clear command window.
clear;  % Delete all variables.
close all;  % Close all figure windows except those created by imtool.
imtool close all;  % Close all figure windows created by imtool.
workspace;  % Make sure the workspace panel is showing.
fontSize = 15;
% Read in a standard MATLAB demo image.
folder = fullfile(matlabroot, '\toolbox\images\imdemos');
baseFileName = 'coins.png';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
if ~exist(fullFileName, 'file')
  % Didn't find it there.  Check the search path for it.
  fullFileName = baseFileName; % No path this time.
  if ~exist(fullFileName, 'file')
    % Still didn't find it.  Alert user.
    errorMessage = sprintf('Error: %s does not exist.', fullFileName);
    uiwait(warndlg(errorMessage));
    return;
  end
end
grayImage = imread(fullFileName);
% Get the dimensions of the image.  numberOfColorBands should be = 1.
[rows columns numberOfColorBands] = size(grayImage);
% Display the original image.
subplot(2, 2, 1);
imshow(grayImage);
title('Original Gray Scale Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'Position', get(0,'Screensize')); 
% Generate a noisy image with salt and pepper noise.
noisyImage = imnoise(grayImage,'salt & pepper', 0.05);
subplot(2, 2, 2);
imshow(noisyImage);
title('Image with Salt and Pepper Noise', 'FontSize', fontSize);
% ===============================================
% Everything before was just to get a noisy image.
% Here's where the algorithm really starts.  
% Median Filter the image:
  medianFilteredImage = medfilt2(noisyImage, [3 3]);
% Find the noise.  It will have a gray level of either 0 or 255.
noiseImage = (noisyImage == 0 | noisyImage == 255);
% Get rid of the noise by replacing with median.
noiseFreeImage = noisyImage; % Initialize
noiseFreeImage(noiseImage) = medianFilteredImage(noiseImage); % Replace.
% Display the image.
subplot(2, 2, 3);
imshow(noiseFreeImage);
title('Restored Image', 'FontSize', fontSize);