MATLAB: How to add to an image white Gaussian noise of zero mean and standard deviation of certain gray levels

gaussian noiseimage processingImage Processing Toolbox

Hello everyone, How can we add white Gaussian noise to an image with zero mean and standard deviation of 64 gray levels? I do know how to add noise of zero mean and variance using imnoise but I do not know about standard deviation of 64 gray levels.

Best Answer

Did you try imnoise() or randn()? If not, why not? They're so easy that you should be able to figure them out on your own.

Related Solutions

MATLAB: Average local variance

Again, I'm not sure what this means or if it is really what you want but I think it's what you described.

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
imtool close all;  % Close all imtool figures.
clear;  % Erase all existing variables.
workspace;  % Make sure the workspace panel is showing.
fontSize = 20;
% Read in a standard MATLAB gray scale demo image.
folder = fullfile(matlabroot, '\toolbox\images\imdemos');
baseFileName = 'cameraman.tif';
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
grayImage = imread(fullFileName);
% Get the dimensions of the image.  
% numberOfColorBands should be = 1.
[rows columns numberOfColorBands] = size(grayImage);
% Display the original gray scale image.
subplot(2, 3, 1);
imshow(grayImage, []);
title('Original Grayscale Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Give a name to the title bar.
set(gcf,'name','Demo by ImageAnalyst','numbertitle','off') 
% Calculate the standard deviation image.
sdImage = stdfilt(grayImage);
% Display the image.


subplot(2, 3, 2);
imshow(sdImage, []);
title('Std. Dev. Image', 'FontSize', fontSize);
% Calculate the variance image.
varianceImage = sdImage .^2;
% Display the image.
subplot(2, 3, 3);
imshow(varianceImage, []);
title('Variance Image', 'FontSize', fontSize);
% Calculate the mean of the variance image in a 3x3 region.
% Basically this is a blurred version of it.
meanVarianceImage = conv2(varianceImage, ones(3)/9);
% Display the image.
subplot(2, 3, 4);
imshow(meanVarianceImage, []);
title('Mean Variance Image', 'FontSize', fontSize);
for gl = 0:255
  % Find pixels in the original image with this gray level.
  matchingIndexes = (grayImage == gl);
  % Get the mean of only those pixels from the mean variance image.
  W(gl+1) = mean(meanVarianceImage(matchingIndexes));
end
% Plot W
subplot(2, 3, 5);
plot(W);
grid on;
title('Plot of W', 'FontSize', fontSize);
xlabel('Gray Level', 'FontSize', fontSize);

MATLAB: Compare the levels of gray

See this snippet

% Let's compute and display the histogram.
[pixelCount, grayLevels] = imhist(grayImage);
bar(grayLevels, pixelCount);
grid on;
title('Histogram of image', 'FontSize', 22);
xlim([0 255]); % or 65535 if image is uint16
% Compute and display mean
meanGrayLevel = mean2(grayImage);
message = sprintf('The mean gray level is %.2f', meanGrayLevel);
uiwait(helpdlg(message));

Best Answer

Related Solutions

MATLAB: Average local variance

MATLAB: Compare the levels of gray

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