matlab-(含教程)基于快速引导滤波和焦点像素检测的多焦点图像融合matlab仿真

function q = fastguidedfilter_color(I, p, r, eps, s) I_sub = imresize(I, 1/s, 'nearest'); % NN is often enough p_sub = imresize(p, 1/s, 'nearest'); r_sub = r / s; % make sure this is an integer [hei, wid] = size(p_sub); N = boxfilter(ones(hei, wid), r_sub); % the size of each local patch; N=(2r+1)^2 except for boundary pixels. mean_I_r = boxfilter(I_sub(:, :, 1), r_sub) ./ N; mean_I_g = boxfilter(I_sub(:, :, 2), r_sub) ./ N; mean_I_b = boxfilter(I_sub(:, :, 3), r_sub) ./ N; mean_p = boxfilter(p_sub, r_sub) ./ N; mean_Ip_r = boxfilter(I_sub(:, :, 1).*p_sub, r_sub) ./ N; mean_Ip_g = boxfilter(I_sub(:, :, 2).*p_sub, r_sub) ./ N; mean_Ip_b = boxfilter(I_sub(:, :, 3).*p_sub, r_sub) ./ 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_sub(:, :, 1).*I_sub(:, :, 1), r_sub) ./ N - mean_I_r .* mean_I_r; var_I_rg = boxfilter(I_sub(:, :, 1).*I_sub(:, :, 2), r_sub) ./ N - mean_I_r .* mean_I_g; var_I_rb = boxfilter(I_sub(:, :, 1).*I_sub(:, :, 3), r_sub) ./ N - mean_I_r .* mean_I_b; var_I_gg = boxfilter(I_sub(:, :, 2).*I_sub(:, :, 2), r_sub) ./ N - mean_I_g .* mean_I_g; var_I_gb = boxfilter(I_sub(:, :, 2).*I_sub(:, :, 3), r_sub) ./ N - mean_I_g .* mean_I_b; var_I_bb = boxfilter(I_sub(:, :, 3).*I_sub(:, :, 3), r_sub) ./ 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)]; 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)); % very inefficient. Replace this in your C++ code. 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; mean_a(:, :, 1) = boxfilter(a(:, :, 1), r_sub)./N; mean_a(:, :, 2) = boxfilter(a(:, :, 2), r_sub)./N; mean_a(:, :, 3) = boxfilter(a(:, :, 3), r_sub)./N; mean_b = boxfilter(b, r_sub)./N; mean_a = imresize(mean_a, [size(I, 1), size(I, 2)], 'bilinear'); % bilinear is recommended mean_b = imresize(mean_b, [size(I, 1), size(I, 2)], 'bilinear'); q = mean_a(:, :, 1) .* I(:, :, 1)... + mean_a(:, :, 2) .* I(:, :, 2)... + mean_a(:, :, 3) .* I(:, :, 3)... + mean_b; end