论文"Gradient Domain Guided Image Filtering" matlab实现代码

论文"Kou F, Chen W, Wen C, et al. Gradient Domain Guided Image Filtering[J]. Image Processing, IEEE Transactions on, 2015, 24(11): 4528-4539." matlab实现代码。

主程序

function q = gradient_guidedfilter(I, p, eps)
%   GUIDEDFILTER   O(1) time implementation of guided filter.
%
%   - guidance image: I (should be a gray-scale/single channel image)
%   - filtering input image: p (should be a gray-scale/single channel image)
%   - regularization parameter: eps

r=16;
[hei, wid] = size(I);
N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.

mean_I = boxfilter(I, r) ./ N;
mean_p = boxfilter(p, r) ./ N;
mean_Ip = boxfilter(I.*p, r) ./ N;
cov_Ip = mean_Ip - mean_I .* mean_p; % this is the covariance of (I, p) in each local patch.

mean_II = boxfilter(I.*I, r) ./ N;
var_I = mean_II - mean_I .* mean_I;

%weight
epsilon=(0.001*(max(p(:))-min(p(:))))^2;
r1=1;

N1 = boxfilter(ones(hei, wid), r1); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.
mean_I1 = boxfilter(I, r1) ./ N1;
mean_II1 = boxfilter(I.*I, r1) ./ N1;
var_I1 = mean_II1 - mean_I1 .* mean_I1;

chi_I=sqrt(abs(var_I1.*var_I));    
weight=(chi_I+epsilon)/(mean(chi_I(:))+epsilon);     

gamma = (4/(mean(chi_I(:))-min(chi_I(:))))*(chi_I-mean(chi_I(:)));
gamma = 1 - 1./(1 + exp(gamma));

%result
a = (cov_Ip + (eps./weight).*gamma) ./ (var_I + (eps./weight)); 
b = mean_p - a .* mean_I; 

mean_a = boxfilter(a, r) ./ N;
mean_b = boxfilter(b, r) ./ N;

q = mean_a .* I + mean_b; 
end

子程序boxfilter()

function imDst = boxfilter(imSrc, r)

%   BOXFILTER   O(1) time box filtering using cumulative sum
%
%   - Definition imDst(x, y)=sum(sum(imSrc(x-r:x+r,y-r:y+r)));
%   - Running time independent of r; 
%   - Equivalent to the function: colfilt(imSrc, [2*r+1, 2*r+1], 'sliding', @sum);
%   - But much faster.

[hei, wid] = size(imSrc);
imDst = zeros(size(imSrc));

%cumulative sum over Y axis
imCum = cumsum(imSrc, 1);
%difference over Y axis
imDst(1:r+1, :) = imCum(1+r:2*r+1, :);
imDst(r+2:hei-r, :) = imCum(2*r+2:hei, :) - imCum(1:hei-2*r-1, :);
imDst(hei-r+1:hei, :) = repmat(imCum(hei, :), [r, 1]) - imCum(hei-2*r:hei-r-1, :);

%cumulative sum over X axis
imCum = cumsum(imDst, 2);
%difference over X axis
imDst(:, 1:r+1) = imCum(:, 1+r:2*r+1);
imDst(:, r+2:wid-r) = imCum(:, 2*r+2:wid) - imCum(:, 1:wid-2*r-1);
imDst(:, wid-r+1:wid) = repmat(imCum(:, wid), [1, r]) - imCum(:, wid-2*r:wid-r-1);
end

运行程序

clear

I = double(imread('D:\数字图像处理\研究方向\Filter Smooth\images\tulips.png')) / 255;
% if size(I,3)==3
%     I=rgb2gray(I);
% end

p = I;
r=16;
eps = 0.8^2; % try eps=0.1^2, 0.2^2, 0.4^2

q_guide(:,:,1)=guidedfilter(I(:,:,1), p(:,:,1), r, eps);
q_guide(:,:,2)=guidedfilter(I(:,:,2), p(:,:,2), r, eps);
q_guide(:,:,3)=guidedfilter(I(:,:,3), p(:,:,3), r, eps);

q(:,:,1) = gradient_guidedfilter(I(:,:,1), p(:,:,1), eps);
q(:,:,2) = gradient_guidedfilter(I(:,:,2), p(:,:,2), eps);
q(:,:,3) = gradient_guidedfilter(I(:,:,3), p(:,:,3), eps);

figure;imshow([I,q_guide,q]);title('原图，引导滤波，改进引导滤波 eps=0.8^2');

下面是效果图