How to implement convolution instead of the built-in imfilter

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m=13; n=13;
sigma=15;
[h1 h2]=meshgrid(-(m-1)/2:(m-1)/2, -(n-1)/2:(n-1)/2);
hg= exp(-(h1.^2+h2.^2)/(2*sigma^2)); %Gaussian function
h=hg ./sum(hg(:));
Now I want to use these Gaussian kernels to implement linear filtering with on image
OriginalRGB = imread('LeerNaam.png'); % read image
filter=h; s = size(OriginalRGB);
r = zeros(s);
for i = 2:s(1)-1
for j = 2:s(2)-1
temp = OriginalRGB(i-1:i+1,j-1:j+1)) .* filter;
r(i,j) = sum(temp(:));
end
end
  4 Comments
Image Analyst
Image Analyst on 10 Jul 2013
Don't do that - it's not linear filtering, that's masking. See my demo below.
Ferdie
Ferdie on 11 Jul 2013
Thanks... The reason for not implementing imfilter is because I need to know what imfilter does. It is for a project and I can't just use built-in functions.

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Accepted Answer

Image Analyst
Image Analyst on 10 Jul 2013
Try this demo:
clc; % Clear the command window.
clearvars; % Erase all existing variables.
workspace; % Make sure the workspace panel is showing.
format longg;
format compact;
fontSize = 20;
close all;
% Check that user has the Image Processing Toolbox installed.
hasIPT = license('test', 'image_toolbox');
if ~hasIPT
% User does not have the toolbox installed.
message = sprintf('Sorry, but you do not seem to have the Image Processing Toolbox.\nDo you want to try to continue anyway?');
reply = questdlg(message, 'Toolbox missing', 'Yes', 'No', 'Yes');
if strcmpi(reply, 'No')
% User said No, so exit.
return;
end
end
% Read in a standard MATLAB color demo image.
folder = fullfile(matlabroot, '\toolbox\images\imdemos');
baseFileName = 'peppers.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
rgbImage = imread(fullFileName);
% Get the dimensions of the image. numberOfColorBands should be = 3.
[rows, columns, numberOfColorBands] = size(rgbImage);
% Display the original color image.
subplot(2, 1, 1);
imshow(rgbImage);
title('Original Color Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Extract the individual red, green, and blue color channels.
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
% Construct Gaussian Kernel
m=13;
n=13;
sigma=15;
[h1 h2]=meshgrid(-(m-1)/2:(m-1)/2, -(n-1)/2:(n-1)/2);
hg= exp(-(h1.^2+h2.^2)/(2*sigma^2)); %Gaussian function
h=hg ./sum(hg(:))
% Could be done easier with fspecial though!
% Convolve the three separate color channels.
redBlurred = conv2(redChannel, h);
greenBlurred = conv2(greenChannel, h);
blueBlurred = conv2(blueChannel, h);
% Recombine separate color channels into a single, true color RGB image.
rgbImage2 = cat(3, uint8(redBlurred), uint8(greenBlurred), uint8(blueBlurred));
% Display the blurred color image.
subplot(2, 1, 2);
imshow(rgbImage2);
title('Blurred Color Image', 'FontSize', fontSize);
  1 Comment
Ferdie
Ferdie on 11 Jul 2013
Thank you very much! But I also need to do the convolution without using the conv2 built in function. I tried for loops, but I have problem when doing .* multiplication. Matlab gives errors. How can I code conv2?

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