1 files changed, 59 insertions, 0 deletions

diff --git a/MATLAB/guidedfilter_color.m b/MATLAB/guidedfilter_color.m
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index 0000000..226b5df
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+++ b/MATLAB/guidedfilter_color.m
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+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

+

+[hei, wid] = size(p);

+N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.

+

+mean_I_r = boxfilter(I(:, :, 1), r) ./ N;

+mean_I_g = boxfilter(I(:, :, 2), r) ./ N;

+mean_I_b = boxfilter(I(:, :, 3), r) ./ N;

+

+mean_p = boxfilter(p, r) ./ N;

+

+mean_Ip_r = boxfilter(I(:, :, 1).*p, r) ./ N;

+mean_Ip_g = boxfilter(I(:, :, 2).*p, r) ./ N;

+mean_Ip_b = boxfilter(I(:, :, 3).*p, r) ./ N;

+

+% covariance of (I, p) in each local patch.

+cov_Ip_r = mean_Ip_r - mean_I_r .* mean_p;

+cov_Ip_g = mean_Ip_g - mean_I_g .* mean_p;

+cov_Ip_b = mean_Ip_b - mean_I_b .* mean_p;

+

+% 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_r .*  mean_I_r; 

+var_I_rg = boxfilter(I(:, :, 1).*I(:, :, 2), r) ./ N - mean_I_r .*  mean_I_g; 

+var_I_rb = boxfilter(I(:, :, 1).*I(:, :, 3), r) ./ N - mean_I_r .*  mean_I_b; 

+var_I_gg = boxfilter(I(:, :, 2).*I(:, :, 2), r) ./ N - mean_I_g .*  mean_I_g; 

+var_I_gb = boxfilter(I(:, :, 2).*I(:, :, 3), r) ./ N - mean_I_g .*  mean_I_b; 

+var_I_bb = boxfilter(I(:, :, 3).*I(:, :, 3), r) ./ N - mean_I_b .*  mean_I_b; 

+

+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_Ip = [cov_Ip_r(y, x), cov_Ip_g(y, x), cov_Ip_b(y, x)];        

+        

+        a(y, x, :) = cov_Ip * inv(Sigma + eps * eye(3)); % Eqn. (14) in the paper;

+    end

+end

+

+b = mean_p - a(:, :, 1) .* mean_I_r - a(:, :, 2) .* mean_I_g - a(:, :, 3) .* mean_I_b; % 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
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