Complex image processing for loop vectorization
1 visualizzazione (ultimi 30 giorni)
Mostra commenti meno recenti
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 Commenti
Rik
il 21 Mag 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.
Risposte (1)
Image Analyst
il 21 Mag 2021
I would not reuse that code. I'd use the built-in imguidedfilter() function.
3 Commenti
Rik
il 25 Mag 2021
Would it be possible to write an interface function that converts the input parameters to what imguidedfilter needs?
Vedere anche
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!