引导滤波 Guided Image Filtering

本文主要介绍导向滤波，引导滤波在滤波的同时，具有保边缘的特效，考虑了空间因素，可以用在图像去雾算法中对透射率t的优化中，能起到很好的效果

论文如下：

Guided Image Filtering
Kaiming He , Jian Sun , and Xiaoou Tang ,
Department of Information Engineering, The Chinese University of Hong Kong
Microsoft Research Asia
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

原理如下：

代码如下：

#include "afxwin.h"
#include <cv.h>
#include "cxcore.h"
#include <highgui.h>
#include<vector>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"   void Ctry::OnTryTyr1()  //消息响应函数
{int r = 4;double eps = 0.01;Mat image_src = imread("C:\\Users\\Administrator\\Desktop\\38.jpg", CV_LOAD_IMAGE_COLOR);vector<Mat> bgr_src, bgr_dst;split(image_src, bgr_src);//分解每个通道  Mat dst_color;Mat temp;for (int i = 0; i<3; i++){Mat I = getimage(bgr_src[i]);Mat p = I.clone();Mat q = guidedFilter2(I, p, r, eps); bgr_dst.push_back(q);}merge(bgr_dst, dst_color);imwrite("C:\\Users\\Administrator\\Desktop\\result2.jpg", dst_color*255);double time2 = 0;time2 = (double)getTickCount();Mat image_gray(image_src.size(), CV_8UC1);cvtColor(image_src, image_gray, CV_BGR2GRAY);Mat I = getimage(image_gray);Mat p = I.clone();Mat  Output;guidedFilter2(I, p, r, eps).copyTo(Output);imshow("方法：", Output);Mat tt;Output.convertTo(tt, -1,255.0);imwrite("C:\\Users\\Administrator\\Desktop\\mert.JPG", tt);time2 = 1000 * ((double)getTickCount() - time2) / getTickFrequency();cout << endl << "Time of guided filter2 for  runs: " << time2 << " milliseconds." << endl;    waitKey(0);
}//convert image depth to CV_64F
Mat  Ctry::getimage(Mat &a)
{int hei = a.rows;int wid = a.cols;Mat I(hei, wid, CV_64FC1);//convert image depth to CV_64F  a.convertTo(I, CV_64FC1, 1.0 / 255.0);return I;
}Mat  Ctry::guidedFilter2(cv::Mat I, cv::Mat p, int r, double 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*/cv::Mat _I;I.convertTo(_I, CV_64FC1);I = _I;cv::Mat _p;p.convertTo(_p, CV_64FC1);p = _p;//[hei, wid] = size(I);  int hei = I.rows;int wid = I.cols;//N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.  cv::Mat N;cv::boxFilter(cv::Mat::ones(hei, wid, I.type()), N, CV_64FC1, cv::Size(r, r));//mean_I = boxfilter(I, r) ./ N;  cv::Mat mean_I;cv::boxFilter(I, mean_I, CV_64FC1, cv::Size(r, r));//mean_p = boxfilter(p, r) ./ N;  cv::Mat mean_p;cv::boxFilter(p, mean_p, CV_64FC1, cv::Size(r, r));//mean_Ip = boxfilter(I.*p, r) ./ N;  cv::Mat mean_Ip;cv::boxFilter(I.mul(p), mean_Ip, CV_64FC1, cv::Size(r, r));//cov_Ip = mean_Ip - mean_I .* mean_p; % this is the covariance of (I, p) in each local patch.  cv::Mat cov_Ip = mean_Ip - mean_I.mul(mean_p);//mean_II = boxfilter(I.*I, r) ./ N;  cv::Mat mean_II;cv::boxFilter(I.mul(I), mean_II, CV_64FC1, cv::Size(r, r));//var_I = mean_II - mean_I .* mean_I;  cv::Mat var_I = mean_II - mean_I.mul(mean_I);//a = cov_Ip ./ (var_I + eps); % Eqn. (5) in the paper;     cv::Mat a = cov_Ip / (var_I + eps);//b = mean_p - a .* mean_I; % Eqn. (6) in the paper;  cv::Mat b = mean_p - a.mul(mean_I);//mean_a = boxfilter(a, r) ./ N;  cv::Mat mean_a;cv::boxFilter(a, mean_a, CV_64FC1, cv::Size(r, r));mean_a = mean_a / N;//mean_b = boxfilter(b, r) ./ N;  cv::Mat mean_b;cv::boxFilter(b, mean_b, CV_64FC1, cv::Size(r, r));mean_b = mean_b / N;//q = mean_a .* I + mean_b; % Eqn. (8) in the paper;  cv::Mat q = mean_a.mul(I) + mean_b;return q;
}

效果图：

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