Question

I would like to take two images and convolve them together in Matlab using the 2D FFT without recourse to the conv2 function. However, I am uncertain with respect to how the matrices should be properly padded and prepared for the convolution.

The mathematical operation is the following:

A * B = C

In the above, * is the convolution operator (Wikipedia link).

The following Matlab program shows the difference between padding and not padding the matrices. I suspect that not padding the matrices results in a circular convolution, but I would like to perform a linear convolution without aliasing.

If I do pad the two matrices, then how do I truncate the output of the convolution so that C is the same size as A and B?

A = rgb2gray(im2double(imread('1.png'))); % input A
B = rgb2gray(im2double(imread('2.png'))); % kernel B

figure;
imagesc(A); colormap gray;
title ('A')

figure;
imagesc(B); colormap gray;
title ('B')

[m,n] = size(A);
mm = 2*m - 1;
nn = 2*n - 1;

C = (ifft2(fft2(A,mm,nn).* fft2(B,mm,nn)));

figure;
imagesc(C); colormap gray;
title ('C with padding')

C0 = (ifft2(fft2(A).* fft2(B)));

figure;
imagesc(C0); colormap gray;
title ('C without padding')

Here is the output of the program:

Answer 1

Without padding the result will be equivalent to circular convolution as you point out. For linear convolution, in convolving 2 images (2D signals) A*B the full output will be of size Ma+Mb-1 x Na+Nb-1, where Ma x Na, Mb x Nb the sizes of images A and B resp.

After padding to the expected size, multiplying and transforming back, via ifft2, you can keep the central part of the resulting image (usually corresponding to the largest one of A and B).

A = double(imread('cameraman.tif'))./255; % image
B = fspecial('gaussian', [15 15], 2); % some 2D filter function

[m,n] = size(A);
[mb,nb] = size(B); 
% output size 
mm = m + mb - 1;
nn = n + nb - 1;

% pad, multiply and transform back
C = ifft2(fft2(A,mm,nn).* fft2(B,mm,nn));

% padding constants (for output of size == size(A))
padC_m = ceil((mb-1)./2);
padC_n = ceil((nb-1)./2);

% frequency-domain convolution result
D = C(padC_m+1:m+padC_m, padC_n+1:n+padC_n); 
figure; imshow(D,[]);

Now, compare the above with doing spatial-domain convolution, using conv2D

 % space-domain convolution result
 F = conv2(A,B,'same');
 figure; imshow(F,[]);

Results are visually the same, and total error between the two (due to rounding) on the order of e-10.