前言

之前写过这篇论文的复现，https://blog.csdn.net/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::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;
}int rows, cols;
//获取最小值矩阵
int **getMinChannel(cv::Mat img) {rows = img.rows;cols = img.cols;if (img.channels() != 3) {fprintf(stderr, "Input Error!");exit(-1);}int **imgGray;imgGray = new int *[rows];for (int i = 0; i < rows; i++) {imgGray[i] = new int[cols];}for (int i = 0; i < rows; i++) {for (int j = 0; j < cols; j++) {int loacalMin = 255;for (int k = 0; k < 3; k++) {if (img.at<Vec3b>(i, j)[k] < loacalMin) {loacalMin = img.at<Vec3b>(i, j)[k];}}imgGray[i][j] = loacalMin;}}return imgGray;
}//求暗通道
int **getDarkChannel(int **img, int blockSize = 3) {if (blockSize % 2 == 0 || blockSize < 3) {fprintf(stderr, "blockSize is not odd or too small!");exit(-1);}//计算pool Sizeint poolSize = (blockSize - 1) / 2;int newHeight = rows + poolSize - 1;int newWidth = cols + poolSize - 1;int **imgMiddle;imgMiddle = new int *[newHeight];for (int i = 0; i < newHeight; i++) {imgMiddle[i] = new int[newWidth];}for (int i = 0; i < newHeight; i++) {for (int j = 0; j < newWidth; j++) {if (i < rows && j < cols) {imgMiddle[i][j] = img[i][j];}else {imgMiddle[i][j] = 255;}}}int **imgDark;imgDark = new int *[rows];for (int i = 0; i < rows; i++) {imgDark[i] = new int[cols];}int localMin = 255;for (int i = poolSize; i < newHeight - poolSize; i++) {for (int j = poolSize; j < newWidth - poolSize; j++) {for (int k = i - poolSize; k < i + poolSize + 1; k++) {for (int l = j - poolSize; l < j + poolSize + 1; l++) {if (imgMiddle[k][l] < localMin) {localMin = imgMiddle[k][l];}}}imgDark[i - poolSize][j - poolSize] = localMin;}}return imgDark;
}struct node {int x, y, val;node() {}node(int _x, int _y, int _val) :x(_x), y(_y), val(_val) {}bool operator<(const node &rhs) {return val > rhs.val;}
};//估算全局大气光值
int getGlobalAtmosphericLightValue(int **darkChannel, cv::Mat img, bool meanMode = false, float percent = 0.001) {int size = rows * cols;std::vector <node> nodes;for (int i = 0; i < rows; i++) {for (int j = 0; j < cols; j++) {node tmp;tmp.x = i, tmp.y = j, tmp.val = darkChannel[i][j];nodes.push_back(tmp);}}sort(nodes.begin(), nodes.end());int atmosphericLight = 0;if (int(percent*size) == 0) {for (int i = 0; i < 3; i++) {if (img.at<Vec3b>(nodes[0].x, nodes[0].y)[i] > atmosphericLight) {atmosphericLight = img.at<Vec3b>(nodes[0].x, nodes[0].y)[i];}}}//开启均值模式if (meanMode == true) {int sum = 0;for (int i = 0; i < int(percent*size); i++) {for (int j = 0; j < 3; j++) {sum = sum + img.at<Vec3b>(nodes[i].x, nodes[i].y)[j];}}}//获取暗通道在前0.1%的位置的像素点在原图像中的最高亮度值for (int i = 0; i < int(percent*size); i++) {for (int j = 0; j < 3; j++) {if (img.at<Vec3b>(nodes[i].x, nodes[i].y)[j] > atmosphericLight) {atmosphericLight = img.at<Vec3b>(nodes[i].x, nodes[i].y)[j];}}}return atmosphericLight;
}//恢复原图像
// Omega 去雾比例 参数
//t0 最小透射率值
cv::Mat getRecoverScene(cv::Mat img, float omega = 0.95, float t0 = 0.1, int blockSize = 15, bool meanModel = false, float percent = 0.001) {int** imgGray = getMinChannel(img);int **imgDark = getDarkChannel(imgGray, blockSize = blockSize);int atmosphericLight = getGlobalAtmosphericLightValue(imgDark, img, meanModel = meanModel, percent = percent);float **imgDark2, **transmission;imgDark2 = new float *[rows];for (int i = 0; i < rows; i++) {imgDark2[i] = new float[cols];}Mat B(rows, cols, CV_8UC1);for (int i = 0; i < rows; i++) {for (int j = 0; j < cols; j++) {B.at<uchar>(i, j) = img.at<Vec3b>(i, j)[0];}}Mat p(rows, cols, CV_64FC1);for (int i = 0; i < rows; i++) {for (int j = 0; j < cols; j++) {p.at<double>(i, j) = 1 - omega * imgDark[i][j] / atmosphericLight;}}Mat transmission_filter = guidedFilter(B, p, 80, 1e-3);imshow("transmission", transmission_filter);imwrite("F:\\res3.jpg", transmission_filter);waitKey(0);transmission = new float *[rows];for (int i = 0; i < rows; i++) {transmission[i] = new float[cols];}for (int i = 0; i < rows; i++) {for (int j = 0; j < cols; j++) {imgDark2[i][j] = float(imgDark[i][j]);//transmission[i][j] = 1 - omega * imgDark[i][j] / atmosphericLight;transmission[i][j] = transmission_filter.at<double>(i, j);if (transmission[i][j] < 0.1) {transmission[i][j] = 0.1;}}}cv::Mat dst(img.rows, img.cols, CV_8UC3);for (int channel = 0; channel < 3; channel++) {for (int i = 0; i < rows; i++) {for (int j = 0; j < cols; j++) {int temp = (img.at<Vec3b>(i, j)[channel] - atmosphericLight) / transmission[i][j] + atmosphericLight;if (temp > 255) {temp = 255;}if (temp < 0) {temp = 0;}dst.at<Vec3b>(i, j)[channel] = temp;}}}return dst;
}int main() {Mat src = imread("F:\\fog\\3.jpg");//cv::Mat src = cv::imread("/home/zxy/CLionProjects/Acmtest/3.jpg");rows = src.rows;cols = src.cols;cv::Mat dst = getRecoverScene(src);cv::imshow("origin", src);cv::imshow("result", dst);cv::imwrite("F:\\res2.jpg", dst);waitKey(0);
}

效果

结论

可以看到效果确实好一些，关于导向滤波可以看我的这篇博客：https://blog.csdn.net/just_sort/article/details/84324239

《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)相关推荐

《Single Image Haze Removal Using Dark Channel Prior》一文中图像去雾算法的原理、实现、效果及其他。...
在图像去雾这个领域,几乎没有人不知道<Single Image Haze Removal Using Dark Channel Prior>这篇文章,该文是2009年CVPR最佳论文.作者 ...
Single Image Haze Removal Using Dark Channel Prior 基于暗原色先验的单一图像去雾方法【翻译】
Single Image Haze Removal Using Dark Channel Prior Kaiming He, Jian Sun, Xiaoou Tang The Chinese Uni ...
Single Image Haze Removal Using Dark Channel Prior
单幅图像基于暗通道先验的去雾 Single Image Haze Removal Using Dark Channel Prior Kaiming He, Jian Sun, and Xiaoou T ...
《Single Image Haze Removal Using Dark Channel Prior》一文中图像去雾算法的原理、实现、效果（速度可实时）...
最新的效果见 :http://video.sina.com.cn/v/b/124538950-1254492273.html 可处理视频的示例:视频去雾效果在图像去雾这个领域,几乎没有人不知道< ...
《Single Image Haze Removal Using Dark Channel Prior》一文中图像去雾算法的原理、实现、效果及其他。 ...
在图像去雾这个领域,几乎没有人不知道<Single Image Haze Removal Using Dark Channel Prior>这篇文章,该文是2009年CVPR最佳论文.作者 ...
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior——基于颜色衰减先验单图片去雾算法原理...
本人最近在研究去雾方面的最新文献(2016年初),当然去雾方面的经典论文是何凯明博士的<Single Image Haze Removal Using Dark Channel Prior> ...
Contrast in Haze Removal: Configurable Contrast Enhancement Model Based on Dark Channel Prior
吐槽吐槽!!!有史以来读的最费劲的一篇文章,各种长句!各种句式!各种缩写!各种实验中夹杂公式!!!各种各种!!!可是是IEEE的文章呢,读了那么多英文文章的如今读到我开始怀疑我的英文水平了,怀疑人生了 ...
读A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior
学习目标: A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior 个人体会: 基于模糊图像中像素的亮度和饱和 ...
【深度学习】何恺明经典之作—2009 CVPR Best Paper | Dark Channel Prior
本文介绍一下中国大陆第一篇计算机视觉顶会的best paper,也是何恺明第一次在计算机视觉领域大放异彩的一篇经典之作-Dark Channel Prior.另外本文在Dark Channel Pri ...

《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

算法原理

代码

效果

结论

《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)相关推荐

最新文章

热门文章