Custom Image Spatial Filtering Code using loops not giving the same result as using the built in imtransform() function

Hi, I'm working on trying to create a custom code to apply spatial filtering without Matlab functions for school. So I created a custom convolution function to be applied to an image and a kernel but the resultant image looks different for both of these images and I'm hitting a wall with why. My custom code is more blurred and I think my convolution function is incorrect but to me, it looks like I'm applying the equation correctly.

Any help would be appreciated. Thanks.

 clear all; close all; clc;
 % Read images 1 and 2
 im = imread('img1.png');
 A = rgb2gray(im);

    % Read Kernels
Kernel_1 = (1/9)*ones(3);
Kernel_2 = (1/49)*ones(7);
Kernel = Kernel_2;          % Set current kernel

 img_out = convolution(A,Kernel);   %Perform convolution on image and selected kernel
 img_out_filter = imfilter(A,Kernel,'same','conv');

 %%Display output images
subplot(1,3,1); imshow(A); title('Original') 
subplot(1,3,2); imshow(img_out,[]); title('Custom Created Function')
subplot(1,3,3); imshow(img_out_filter); title('Matlab imfilter Function')

    %%Rotate input matrix
function rot_mat = rot(mat,theta)
    theta = -25 *2*pi/360;
    R = [cos(theta)  sin(theta) 0;
         -sin(theta) cos(theta) 0;
         0           0          1];
    rot_mat = mat*R;
end

    %%Perform convolution on image and kernel
function B = convolution(A, k)
    [ky, kx] = size(k);             % Read kernel size
    im_pad = padarray(A, [kx ky]);  % Pad original image
    [y, x] = size(im_pad);          % Read image size

    B = zeros(x,y);         % Create empty matrix to store output image
    kr = rot90(k);          % Rotates kernel 180 deg for convolution
    kr = rot90(kr);

    for i=(1+ky):(y-ky)         % index through each image row
        for j=(1+kx):(x-kx)     % index through each image pixel        
            neigh = im_pad(i-floor(ky/2):i+floor(ky/2), j-floor(kx/2):j+floor(kx/2));   % Create local neighborhood of image
            accumulator = 0; 
            for u=1:ky      % index through each kernel row 
                for v=1:kx  % index through each kernel element
                    if(i>ky && i<y-ky && j>kx && j<y-kx)
                    temp = neigh(u,v)*kr(u,v);
                    accumulator = accumulator + temp;
                    end
                end
            end
            B(i,j) = accumulator;   %Set value of pixel in new image with convolution operation resultant
        end
    end
    B=B(1+ky:y-ky,1+kx:x-kx);       % Remove image padding
end

Answer 1

[Anton Semechko]

Actually, both your 'convolution' function and built-in 'imfilter' function produce very similar results. The discrepancy you were observing was due to the fact that you forgot to cast A (the image) into double format before filtering. Here is the corrected part of your code.

 % Read images 1 and 2
 im = imread('img1.png');
 A = rgb2gray(im);
 A = double(A);

    % Read Kernels
Kernel_1 = (1/9)*ones(3);
Kernel_2 = (1/49)*ones(7);
Kernel = Kernel_2;          % Set current kernel

 img_out = convolution(A,Kernel);   %Perform convolution on image and selected kernel
 img_out_filter = imfilter(A,Kernel,'same','conv');

 %%Display output images
figure('color','w')
subplot(1,3,1); imshow(A,[]); title('Original') 
subplot(1,3,2); imshow(img_out,[]); title('Custom Created Function')
subplot(1,3,3); imshow(img_out_filter,[]); title('Matlab imfilter Function')

[Joshua Kearney]

Thanks, that makes sense. Casting it with double made it work great! Cheers.

[Anton Semechko]

Glad I could help. You did a good job with your implementation of 2D convolution.

[Anton Semechko]

Also note that there is not need to reflect the filtering kernel if its radially symmetric, like it is in your case.