Complex image processing for loop vectorization
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Hello everyone, I am new to MatLab. I would like to reuse this code but I was wondering if there is a way to vectorize the double for loop ?
link to paper : Guided Image Filtering
function q = guidedfilter_color(I, p, r, eps)
% GUIDEDFILTER_COLOR O(1) time implementation of guided filter using a color image as the guidance.
%
% - guidance image: I (should be a color (RGB) image)
% - filtering input image: p (should be a gray-scale/single channel image)
% - local window radius: r
% - regularization parameter: eps
if ~(size(I,3) == 3)
error('The guidance image input should have 3 channels');
end
[hei, wid] = size(p);
if r<2*min(hei, wid), r = round(min(hei, wid)/4); end;
N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.
mean_I = zeros(size(I));
for ii =1:size(I,3)
mean_I(:,:,ii) = boxfilter(I(:, :, ii), r) ./ N;
end
mean_p = boxfilter(p, r) ./ N;
mean_Ip = zeros(size(I));
for ii =1:size(I,3)
mean_Ip(:,:,ii) = boxfilter(I(:, :, ii).*p, r) ./ N;
end
% covariance of (I, p) in each local patch.
cov_Ip = zeros(size(I));
for ii =1:size(I,3)
cov_Ip(:,:,ii) = mean_Ip(:,:,ii) - mean_I(:,:,ii) .* mean_p;
end
% variance of I in each local patch: the matrix Sigma in Eqn (14).
% Note the variance in each local patch is a 3x3 symmetric matrix:
% rr, rg, rb
% Sigma = rg, gg, gb
% rb, gb, bb
var_I_rr = boxfilter(I(:, :, 1).*I(:, :, 1), r) ./ N - mean_I(:,:,1) .* mean_I(:,:,1);
var_I_rg = boxfilter(I(:, :, 1).*I(:, :, 2), r) ./ N - mean_I(:,:,1) .* mean_I(:,:,2);
var_I_gg = boxfilter(I(:, :, 2).*I(:, :, 2), r) ./ N - mean_I(:,:,2) .* mean_I(:,:,2);
var_I_rb = boxfilter(I(:, :, 1).*I(:, :, 3), r) ./ N - mean_I(:,:,1) .* mean_I(:,:,3);
var_I_gb = boxfilter(I(:, :, 2).*I(:, :, 3), r) ./ N - mean_I(:,:,2) .* mean_I(:,:,3);
var_I_bb = boxfilter(I(:, :, 3).*I(:, :, 3), r) ./ N - mean_I(:,:,3) .* mean_I(:,:,3);
a = zeros(hei, wid, 3);
for y=1:hei
for x=1:wid
Sigma = [var_I_rr(y, x), var_I_rg(y, x), var_I_rb(y, x);
var_I_rg(y, x), var_I_gg(y, x), var_I_gb(y, x);
var_I_rb(y, x), var_I_gb(y, x), var_I_bb(y, x)];
%Sigma = Sigma + eps * eye(3);
cov_Ip1 = [cov_Ip(y, x,1), cov_Ip(y, x,2), cov_Ip(y, x,3)];
a(y, x, :) = cov_Ip1 * inv(Sigma + eps * eye(3)); % Eqn. (14) in the paper;
end
end
b = mean_p - a(:, :, 1) .* mean_I(:,:,1) - a(:, :, 2) .* mean_I(:,:,2) - a(:, :, 3) .* mean_I(:,:,3); % Eqn. (15) in the paper;
q = (boxfilter(a(:, :, 1), r).* I(:, :, 1)...
+ boxfilter(a(:, :, 2), r).* I(:, :, 2)...
+ boxfilter(a(:, :, 3), r).* I(:, :, 3)...
+ boxfilter(b, r)) ./ N; % Eqn. (16) in the paper;
end
4 Comments
Rik
on 21 May 2021
I don't have a ready-made solution for you. You could start yourself by running the profiler to look where your code spends the most time.
Answers (1)
Image Analyst
on 21 May 2021
I would not reuse that code. I'd use the built-in imguidedfilter() function.
3 Comments
Rik
on 25 May 2021
Would it be possible to write an interface function that converts the input parameters to what imguidedfilter needs?
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