Neighborhood operations of the image

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

Answer 1

[Andrei Bobrov]

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));

[Jim]

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]

for image use function 'imfilter', see my answer

Answer 2

[Takuya Otani]

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.

Answer 3

[Image Analyst]

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;