《Single Image Haze Removal Using Dark Channel Prior》论文阅读及复现番外(使用导向滤波估计更加精细的透射率)

前言

之前写过这篇论文的复现，https://blog.youkuaiyun.com/just_sort/article/details/84110518 ，但是当时估算透射率的时候没有用softmating或者导向滤波，是用何博士的公式粗略的计算的，所幸，在了解了导向滤波后，我完成了使用导向滤波估算 更加精细的透射率的任务，也确实取得了更好的去雾效果，所以打算分享一下这个源码。我所有开源的论文实现均在github地址里：https://github.com/BBuf/-Image-processing-algorithm

算法原理

这些就不说了，可以查看之前的博客，直接公布代码吧。

代码

#include <opencv2/opencv.hpp>
#include <iostream>
#include <algorithm>
#include <vector>
using namespace cv;
using namespace std;

cv::Mat guidedFilter(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::Ma