how to Vectorize this for loop?
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I am trying to create a full convolution without the conv2 function. For that, i would like to vectorize this nested loop.
[r,c] = size(A);% size of image A
[m,n] = size(mask);%size of the mask
ab=padarray(A,[2 2]); %Padding zeros in the original image
ab=im2double(ab);
B = zeros(r+m,c+n);
for x = 1 : r+m-1
for y = 1 : n+c-1
for i = 1 : m
for j = 1 : n
B(x, y) = B(x, y) + (ab(x+i-1, y+j-1) * mask(i, j));
end
end
end
end
2 个评论
Jos (10584)
2017-12-1
Why can't you use conv2? (it is rather silly to speed up code that is not optimal)
Another question: why the fixed [2 2] padding?
采纳的回答
Guillaume
2017-12-1
编辑:Guillaume
2017-12-1
Well, the vectorised version of your code is to use conv2! Or ifft2 the fft2 product (with suitable padding).
Otherwise, when you're trying to teach the principle of convolutions you use explicit loops as you have.
The only thing you may change would be to replace the two inner loops by a vectorised operation:
for x = 1 : r+m-1
for y = 1 : n+c-1
B(x, y) = sum(ab(x:x+m-1, y:y+m-1) .* mask)
end
end
I don't understand the fixed size padding. If you're computing a full convolution then you should indeed end up with an array of size size(ab) + size(mask) - 1 but that's because you've padded ab by size(mask).
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