clear all;
close all;
K=1/16.*[1 2 1 ; 2 4 2 ; 1 2 1]; %random filter
I = imread('lenaTest3.jpg');
figure(1);
imshow(I);
title('Original image');
filterSize = length(K); % Filter window's full width.
% Compute the number of layers of pixels with value 0 to surround the image with.
numLayers = floor(filterSize/2) % Will be 1
%% Zero_padding
fprintf('\nZERO PADDING\n');
I_padded = padarray(I, [numLayers, numLayers]); %Zero-padding(zeros around the image)
filteredImage=convolution(I_padded , K , numLayers , filterSize); %add 2 extra parameters numLayers,filterSize
filteredImage = cast(filteredImage, 'like', I); %turn it to uint8 form instead of double
figure(2);
imshow(filteredImage);
title('Manual convolution (zero-padding)');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%my manual convolution function
function convImage = convolution( image,kernel,numLayers , filterSize )
[rows, columns] = size(image);
filteredImage = zeros(size(image)); % Initialize to same size as original.
for col = numLayers + 1 : columns - numLayers
for row = numLayers + 1 : rows - numLayers
% Now for a window with the center pixel situated at (row, col,
% scan the filter window, multiplying its values by the values of the original image underneath it.
localSum = 0; % Initialize sum to zero for this (row, column) location.
for c = 1 : filterSize
% Get the column index of the original image underneath the corresponding pixel in the filter window
ic = col + c - numLayers - 1;
for r = 1 : filterSize
% Get the row index of the original image underneath the corresponding pixel in the filter window
ir = row + r - numLayers - 1;
% Sum up the product into our running total for this window location.
localSum = localSum + double(image(ir, ic)) * kernel(r, c);
end
end
% Now we have the filtered value. Assign it to our output image.
convImage(row, col) = localSum;
end
end
end
