Neighborhood operations of the image

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Jim
Jim 2012-5-5
Hi,
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

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回答（3 个）

Andrei Bobrov
Andrei Bobrov 2012-5-5
try function conv2
eg:
I - your array
[m n] - size your sliding window
solution:
out = conv2(I,ones([m n])/(m*n),'same');
ADD
use function imfilter and fspecial from Image Processing Toolbox
out = imfilter(I,fspecial('average',[m n]));
here I - your image
or:
out = imfilter(I, ones([m n])/(m*n));
  2 个评论
Jim
Jim 2012-5-5
Thank you for ur reply
But I need to use sliding window and block processing here
I should not use conv2 here
I performed the sliding window using nlfilter
and block processing using blkproc(A,[m n],fun)
wat is the difference between these 2 operations?
can u suggest anything about this?
Andrei Bobrov
Andrei Bobrov 2012-5-5
for image use function 'imfilter', see my answer

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Takuya Otani
Takuya Otani 2012-5-5
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 2012-5-5
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()
try
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.
if(~isdeployed)
cd(fileparts(which(mfilename)));
end
% 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(block_struct.data) * ones(size(block_struct.data));
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);
uiwait(warndlg(errorMessage));
end
return;
% 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)
try
% 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');
return;
end
% Size matches if we get here, so we're okay.
% Extract our block out of the structure.
array3x3 = blockStruct.data;
% 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);
end
return;

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