Neighborhood operations of the image

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Jim on 5 May 2012
I have an image of size 256x256.
I need to apply the sliding window and block processing (neighborhood)operations to the image. As a result the pixel in the center of the window should have the mean value of the window.
Can you suggest anything about this?
Thanks in advance

Answers (3)

Andrei Bobrov
Andrei Bobrov on 5 May 2012
try function conv2
I - your array
[m n] - size your sliding window
out = conv2(I,ones([m n])/(m*n),'same');
use function imfilter and fspecial from Image Processing Toolbox
out = imfilter(I,fspecial('average',[m n]));
here I - your image
out = imfilter(I, ones([m n])/(m*n));
Andrei Bobrov
Andrei Bobrov on 5 May 2012
for image use function 'imfilter', see my answer

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Takuya Otani
Takuya Otani on 5 May 2012
Try blockproc function in image processing toolbox It does exactly what you are looking for You may also try colfilt also found in image processing toolbox. It is similar.

Image Analyst
Image Analyst on 5 May 2012
Here's a demo that shows two different ways of using blockproc. Save the code below as blockproc_demo.m and run it.
function blockproc_demo()
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
workspace; % Make sure the workspace panel is showing.
fontSize = 20;
% Change the current folder to the folder of this m-file.
% Read in standard MATLAB demo image.
grayImage = imread('cameraman.tif');
[rows columns numberOfColorChannels] = size(grayImage);
% Display the original image.
subplot(2, 2, 1);
imshow(grayImage, []);
caption = sprintf('Original Image\n%d by %d pixels', ...
rows, columns);
title(caption, 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'Position', get(0,'Screensize'));
set(gcf, 'name','Demo by ImageAnalyst', 'numbertitle','off')
% Block process the image.
windowSize = 3;
% Each 3x3 block will get replaced by one value.
% Output image will be smaller by a factor of windowSize.
myFilterHandle = @myFilter;
blockyImage = blockproc(grayImage,[windowSize windowSize], myFilterHandle);
[rowsP columnsP numberOfColorChannelsP] = size(blockyImage);
% Display the processed image.
% It is smaller, but the display routine imshow() replicates
% the image so that it looks bigger than it really is.
subplot(2, 2, 2);
imshow(blockyImage, []);
caption = sprintf('Image Processed in %d by %d Blocks\n%d by %d pixels\nCustom Box Filter', ...
windowSize, windowSize, rowsP, columnsP);
title(caption, 'FontSize', fontSize);
% Now let's do it an alternate way where we use an anonymous function.
% We'll take the standard deviation in the blocks.
windowSize = 8;
myFilterHandle2 = @(block_struct) ...
std2( * ones(size(;
blockyImageSD = blockproc(grayImage, [windowSize windowSize], myFilterHandle2);
[rowsSD columnsSD numberOfColorChannelsSD] = size(blockyImageSD);
subplot(2, 2, 4);
imshow(blockyImageSD, []);
caption = sprintf('Image Processed in %d by %d Blocks\n%d by %d pixels\nAnonymous Standard Deviation Filter', ...
windowSize, windowSize, rowsSD, columnsSD);
title(caption, 'FontSize', fontSize);
% Note: the image size of blockyImageSD is 256x256, NOT smaller.
% That's because we're returning an 8x8 array instead of a scalar.
uiwait(msgbox('Done with demo'));
catch ME
errorMessage = sprintf('Error in blockproc_demo():\n\nError Message:\n%s', ME.message);
% Takes one 3x3 block of image data and multiplies it
% element by element by the kernel and
% returns a single value.
function singleValue = myFilter(blockStruct)
% Assign default value.
% Will be used near sides of image (due to boundary effects),
% or in the case of errors, etc.
singleValue = 0;
% Create a 2D filter.
kernel = [0 0.2 0; 0.2 0.2 0.2; 0 0.2 0];
% kernel = ones(blockStruct.blockSize); % Box filter.
% Make sure filter size matches image block size.
if any(blockStruct.blockSize ~= size(kernel))
% If any of the dimensions don't match.
% You'll get here near the edges,
% if the image is not a multiple of the block size.
% warndlg('block size does not match kernel size');
% Size matches if we get here, so we're okay.
% Extract our block out of the structure.
array3x3 =;
% Do the filtering. Multiply by kernel and sum.
singleValue = sum(sum(double(array3x3) .* kernel));
catch ME
% Some kind of problem...
errorMessage = sprintf('Error in myFilter():\n\nError Message:\n%s', ME.message);
% uiwait(warndlg(errorMessage));
fprintf(1, '%s\n', errorMessage);

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