opencv 实现导向滤波

from：http://blog.csdn.net/wds555/article/details/23176313

何凯明去雾算法中的导向滤波实现，原文地址导向滤波。

导向图像I，滤波输入图像p以及输出图像q。像素点 i 处的滤波结果是被表达成一个加权平均：





假设导向滤波器在导向图像I和滤波输出q之间是一个局部线性模型：





最小化下面的窗口Wk的代价函数：





用来确定a,b的值





其中


论文所给算法如下：





matlab代码如下：

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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

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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

以下为单通道图像导向滤波opencv实现：

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#include "myGuidedFilter_Mat.h"

CvMat * cumsum(CvMat *src,int rc)

{

CvMat *Imdst = cvCreateMat(src->rows,src->cols,CV_64FC1);

Imdst=cvCloneMat(src);

if (rc==1)

{

for(int y=1;y<src->height;y++)

{

double *ptr0=(double *)(Imdst->data.ptr+(y-1)*Imdst->step);

double *ptr=(double *)(Imdst->data.ptr+y*Imdst->step);

for(int x=0;x<src->width;x++)

{

ptr[x]=ptr0[x]+ptr[x];

//cvSetReal2D(Imdst,y,x,cvGetReal2D(Imdst,y-1,x)+cvGetReal2D(Imdst,y,x));

}

}

}

else if (rc==2)

{

for(int y=0;y<src->height;y++)

{

double *ptr=(double *)(Imdst->data.ptr+y*Imdst->step);

for(int x=1;x<src->width;x++)

{

ptr[x]=ptr[x-1]+ptr[x];

//cvSetReal2D(Imdst,y,x,cvGetReal2D(Imdst,y,x-1)+cvGetReal2D(Imdst,y,x));

}

}

}

return Imdst;

}

CvMat * boxFilter(CvMat *src,int r)

{

CvMat *Imdst = cvCreateMat(src->rows,src->cols,CV_64FC1);

Imdst=cvCloneMat(src);

CvMat *subImage;

//imCum = cumsum(imSrc, 1);

CvMat *imCum = cumsum(Imdst,1);

//imDst(1:r+1, :) = imCum(1+r:2*r+1, :);

for (int y = 0;y<r;y++)

{

//double *ptrDst=(double *)Imdst->data.ptr+y*Imdst->step;

//double *ptrCum=(double *)imCum->data.ptr+(y+r)*imCum->step;

for(int x = 0;x<Imdst->width;x++)

{

//ptrDst[x]=ptrCum[x];

cvSetReal2D(Imdst,y,x,cvGetReal2D(imCum,y+r,x));

}

}

//imDst(r+2:hei-r, :) = imCum(2*r+2:hei, :) - imCum(1:hei-2*r-1, :);

for (int y = r+1;y<Imdst->height-r-1;y++)

{

for(int x = 0;x<Imdst->width;x++)

{

cvSetReal2D(Imdst,y,x,(cvGetReal2D(imCum,y+r,x)-cvGetReal2D(imCum,y-r-1,x)));

}

}

//imDst(hei-r+1:hei, :) = repmat(imCum(hei, :), [r, 1]) - imCum(hei-2*r:hei-r-1, :);

subImage = cvCreateMat(r,Imdst->width,CV_64FC1);

CvMat *tem=cvCreateMat(1,Imdst->width,CV_64FC1);

cvGetRow(imCum,tem,imCum->height-1);

cvRepeat(tem,subImage);

/*for(int y=0;y<r;y++)

{

for(int x=0;x<Imdst->width;x++)

{

cvSetReal2D(subImage,y,x,cvGetReal2D(imCum,Imdst->height-1,x));

}

}*/

for (int y = Imdst->height-r;y<Imdst->height;y++)

{

for(int x = 0;x<Imdst->width;x++)

{

cvSetReal2D(Imdst,y,x,cvGetReal2D(subImage,y-Imdst->height+r,x)-cvGetReal2D(imCum,y-r-1,x));

}

}

cvReleaseMat(&subImage);

cvReleaseMat(&tem);

imCum = cumsum(Imdst, 2);

//imDst(:, 1:r+1) = imCum(:, 1+r:2*r+1);

for (int y = 0;y<Imdst->height;y++)

{

for(int x = 0;x<r;x++)

{

cvSetReal2D(Imdst,y,x,cvGetReal2D(imCum,y,x+r));

}

}

//imDst(:, r+2:wid-r) = imCum(:, 2*r+2:wid) - imCum(:, 1:wid-2*r-1);

for (int y = 0;y<Imdst->height;y++)

{

for(int x = r+1;x<Imdst->width-r-1;x++)

{

cvSetReal2D(Imdst,y,x,(cvGetReal2D(imCum,y,x+r)-cvGetReal2D(imCum,y,x-r-1)));

}

}

//imDst(:, wid-r+1:wid) = repmat(imCum(:, wid), [1, r]) - imCum(:, wid-2*r:wid-r-1);

subImage = cvCreateMat(Imdst->height,r,CV_64FC1);

tem=cvCreateMat(Imdst->height,1,CV_64FC1);

cvGetCol(imCum,tem,imCum->width-1);

cvRepeat(tem,subImage);

/*for(int y=0;y<Imdst->height;y++)

{

for(int x=0;x<r;x++)

{

cvSetReal2D(subImage,y,x,cvGetReal2D(imCum,y,Imdst->width-1));

}

}*/

for (int y = 0;y<Imdst->height;y++)

{

for(int x = Imdst->width-r;x<Imdst->width;x++)

{

cvSetReal2D(Imdst,y,x,cvGetReal2D(subImage,y,x-Imdst->width+r)-cvGetReal2D(imCum,y,x-r-1));

}

}

cvReleaseMat(&subImage);

return Imdst;

}

CvMat * myGuidedFilter_Mat(CvMat * I,CvMat *img_pp,int r, double eps)

{

int height = img_pp->height;

int width = img_pp->width;

int type = CV_64FC1;

CvMat *ones = cvCreateMat(height,width,type);

cvSet(ones,cvRealScalar(1));

CvMat * N = boxFilter(ones,r);

//求I的均值

CvMat * mean_I = cvCreateMat(height,width,type);

cvDiv(boxFilter(I,r),N,mean_I);

//求P的均值

CvMat * mean_p = cvCreateMat(height,width,type);

cvDiv(boxFilter(img_pp,r),N,mean_p);

//求I*P的均值

CvMat * pr = cvCreateMat(height,width,type);

cvMul(I,img_pp,pr);

CvMat * mean_Ip = cvCreateMat(height,width,type);

cvDiv(boxFilter(pr,r),N,mean_Ip);

//求I与p协方差

cvMul(mean_I,mean_p,pr);

CvMat * cov_Ip = cvCreateMat(height,width,type);

cvSub(mean_Ip,pr,cov_Ip);

//求I的方差

CvMat * var_I = cvCreateMat(height,width,type);

cvMul(I,I,pr);

cvDiv(boxFilter(pr,r),N,var_I);

cvMul(mean_I,mean_I,pr);

cvSub(var_I,pr,var_I);

//求a

CvMat * a = cvCreateMat(height,width,type);

cvAddS(var_I,cvScalar(eps),var_I);

cvDiv(cov_Ip,var_I,a);

//求b

CvMat * b = cvCreateMat(height,width,type);

cvMul(a,mean_I,pr);

cvSub(mean_p,pr,b);

//求a的均值

CvMat * mean_a = cvCreateMat(height,width,type);

cvDiv(boxFilter(a,r),N,mean_a);

//求b的均值

CvMat * mean_b = cvCreateMat(height,width,type);

cvDiv(boxFilter(b,r),N,mean_b);

//求Q

CvMat * q = cvCreateMat(height,width,type);

cvMul(mean_a,I,a);

cvAdd(a,mean_b,q);

cvReleaseMat(&ones);

cvReleaseMat(&mean_I);

cvReleaseMat(&mean_p);

cvReleaseMat(&pr);

cvReleaseMat(&mean_Ip);

cvReleaseMat(&cov_Ip);

cvReleaseMat(&var_I);

cvReleaseMat(&a);

cvReleaseMat(&b);

cvReleaseMat(&mean_a);

cvReleaseMat(&mean_b);

return q;

}