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

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Joshua Kearney
Joshua Kearney 2018-6-12
编辑： Anton Semechko 2018-6-12
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

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采纳的回答

Anton Semechko
Anton Semechko 2018-6-12
编辑：Anton Semechko 2018-6-12
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')
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Anton Semechko
Anton Semechko 2018-6-12
编辑：Anton Semechko 2018-6-12
Also note that there is not need to reflect the filtering kernel if its radially symmetric, like it is in your case.

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