矩形滤波代码(计算窗口平均值)

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 Y 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

单通道图像的导向图滤波代码

function q = guidedfilter(I, p, r, 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)

%  - local window radius: r

%  - regularization parameter:

eps

[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;

a = cov_Ip ./ (var_I + eps); % Eqn. (5) in the paper;

b = mean_p - a .* mean_I; % Eqn. (6) in the

paper;

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

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

q = mean_a .* I + mean_b; % Eqn. (8) in the paper;

end

彩色图像的导向图滤波代码

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

图像保边滤波

% example: edge-preserving smoothing

% figure 1 in our paper

close all;

I = double(imread('.\img_smoothing\cat.bmp')) / 255;

p = I;

r = 4; % try r=2, 4, or 8

eps = 0.2^2; % try eps=0.1^2, 0.2^2,

0.4^2

q = guidedfilter(I, p, r, eps);

figure();

imshow([I, q], [0, 1]);

抠图的代码

% example: guided feathering

% figure 9 in our paper

close all;

I = double(imread('.\img_feathering\toy.bmp')) / 255;

p = double(rgb2gray(imread('.\img_feathering\toy-mask.bmp')))

/ 255;

r = 60;

eps = 10^-6;

q = guidedfilter_color(I, p, r, eps);

figure();

imshow([I, repmat(p, [1, 1, 3]), repmat(q, [1, 1, 3])], [0,

1]);