前言

这篇论文实际上也是《快速ACE算法及其在图像拼接中的应用》这篇论文中的快速ACE算法，我用C++实现了，现在放出来。

算法原理

在论文介绍中，提到高动态图像是指在一幅图像中，既有明亮的区域又有阴影区域，为了使细节清晰，需要满足以下几点：
（1）对动态范围具有一定的压缩能力
（2）对亮暗区域的细节有一定的显示能力
（3）满足（1），（2）的条件下不破坏图像的清晰度
Rizzi等根据Retinex理论提出自动颜色均衡算法，该算法考虑了图像中颜色和亮度的空间位置关系，进行局部的自适应滤波，实现具有局部和非线性特点的图像亮度，色度，对比度调整，同时满足灰度世界理论和白斑点假设。

算法步骤

论文中还有一个优化部分，感兴趣可以去看看，并且作者也是有开源代码的。下面给一个C++代码实现，有2种实现，一种是常规实现，另外一种是递归实现，速度稍快。

代码实现

#include <stdio.h>#include <iostream>#include <immintrin.h>#include <opencv2/opencv.hpp>#include <opencv2/core/core.hpp>#include <opencv2/ml/ml.hpp>#include "opencv2/highgui/highgui.hpp"using namespace cv;using namespace cv::ml;using namespace std;
namespace ACE {  //Gray  Mat stretchImage(Mat src) {    int row = src.rows;    int col = src.cols;    Mat dst(row, col, CV_64FC1);    double MaxValue = 0;    double MinValue = 256.0;    for (int i = 0; i < row; i++) {      for (int j = 0; j < col; j++) {        MaxValue = max(MaxValue, src.at<double>(i, j));        MinValue = min(MinValue, src.at<double>(i, j));      }    }    for (int i = 0; i < row; i++) {      for (int j = 0; j < col; j++) {        dst.at<double>(i, j) = (1.0 * src.at<double>(i, j) - MinValue) / (MaxValue - MinValue);        if (dst.at<double>(i, j) > 1.0) {          dst.at<double>(i, j) = 1.0;        }        else if (dst.at<double>(i, j) < 0) {          dst.at<double>(i, j) = 0;        }      }    }    return dst;  }
  Mat getPara(int radius) {    int size = radius * 2 + 1;    Mat dst(size, size, CV_64FC1);    for (int i = -radius; i <= radius; i++) {      for (int j = -radius; j <= radius; j++) {        if (i == 0 && j == 0) {          dst.at<double>(i + radius, j + radius) = 0;        }        else {          dst.at<double>(i + radius, j + radius) = 1.0 / sqrt(i * i + j * j);        }      }    }    double sum = 0;    for (int i = 0; i < size; i++) {      for (int j = 0; j < size; j++) {        sum += dst.at<double>(i, j);      }    }    for (int i = 0; i < size; i++) {      for (int j = 0; j < size; j++) {        dst.at<double>(i, j) = dst.at<double>(i, j) / sum;      }    }    return dst;  }
  Mat NormalACE(Mat src, int ratio, int radius) {    Mat para = getPara(radius);    int row = src.rows;    int col = src.cols;    int size = 2 * radius + 1;    Mat Z(row + 2 * radius, col + 2 * radius, CV_64FC1);    for (int i = 0; i < Z.rows; i++) {      for (int j = 0; j < Z.cols; j++) {        if((i - radius >= 0) && (i - radius < row) && (j - radius >= 0) && (j - radius < col)) {          Z.at<double>(i, j) = src.at<double>(i - radius, j - radius);        }        else {          Z.at<double>(i, j) = 0;        }      }    }
    Mat dst(row, col, CV_64FC1);    for (int i = 0; i < row; i++) {      for (int j = 0; j < col; j++) {        dst.at<double>(i, j) = 0.f;      }    }    for (int i = 0; i < size; i++) {      for (int j = 0; j < size; j++) {        if (para.at<double>(i, j) == 0) continue;        for (int x = 0; x < row; x++) {          for (int y = 0; y < col; y++) {            double sub = src.at<double>(x, y) - Z.at<double>(x + i, y + j);            double tmp = sub * ratio;            if (tmp > 1.0) tmp = 1.0;            if (tmp < -1.0) tmp = -1.0;            dst.at<double>(x, y) += tmp * para.at<double>(i, j);          }        }      }    }    return dst;  }
  Mat FastACE(Mat src, int ratio, int radius) {    int row = src.rows;    int col = src.cols;    if (min(row, col) <= 2) {      Mat dst(row, col, CV_64FC1);      for (int i = 0; i < row; i++) {        for (int j = 0; j < col; j++) {          dst.at<double>(i, j) = 0.5;        }      }      return dst;    }        Mat Rs((row + 1) / 2, (col + 1) / 2, CV_64FC1);        resize(src, Rs, Size((col + 1) / 2, (row + 1) / 2));    Mat Rf= FastACE(Rs, ratio, radius);    resize(Rf, Rf, Size(col, row));    resize(Rs, Rs, Size(col, row));    Mat dst(row, col, CV_64FC1);    Mat dst1 = NormalACE(src, ratio, radius);    Mat dst2 = NormalACE(Rs, ratio, radius);    for (int i = 0; i < row; i++) {      for (int j = 0; j < col; j++) {        dst.at<double>(i, j) = Rf.at<double>(i, j) + dst1.at<double>(i, j) - dst2.at<double>(i, j);      }    }    return dst;  }
  Mat getACE(Mat src, int ratio, int radius) {    int row = src.rows;    int col = src.cols;    vector <Mat> v;    split(src, v);    v[0].convertTo(v[0], CV_64FC1);    v[1].convertTo(v[1], CV_64FC1);    v[2].convertTo(v[2], CV_64FC1);    Mat src1(row, col, CV_64FC1);    Mat src2(row, col, CV_64FC1);    Mat src3(row, col, CV_64FC1);
    for (int i = 0; i < row; i++) {      for (int j = 0; j < col; j++) {        src1.at<double>(i, j) = 1.0 * src.at<Vec3b>(i, j)[0] / 255.0;        src2.at<double>(i, j) = 1.0 * src.at<Vec3b>(i, j)[1] / 255.0;        src3.at<double>(i, j) = 1.0 * src.at<Vec3b>(i, j)[2] / 255.0;      }    }    src1 = stretchImage(FastACE(src1, ratio, radius));    src2 = stretchImage(FastACE(src2, ratio, radius));    src3 = stretchImage(FastACE(src3, ratio, radius));
    Mat dst1(row, col, CV_8UC1);    Mat dst2(row, col, CV_8UC1);    Mat dst3(row, col, CV_8UC1);    for (int i = 0; i < row; i++) {      for (int j = 0; j < col; j++) {        dst1.at<uchar>(i, j) = (int)(src1.at<double>(i, j) * 255);        if (dst1.at<uchar>(i, j) > 255) dst1.at<uchar>(i, j) = 255;        else if (dst1.at<uchar>(i, j) < 0) dst1.at<uchar>(i, j) = 0;        dst2.at<uchar>(i, j) = (int)(src2.at<double>(i, j) * 255);        if (dst2.at<uchar>(i, j) > 255) dst2.at<uchar>(i, j) = 255;        else if (dst2.at<uchar>(i, j) < 0) dst2.at<uchar>(i, j) = 0;        dst3.at<uchar>(i, j) = (int)(src3.at<double>(i, j) * 255);        if (dst3.at<uchar>(i, j) > 255) dst3.at<uchar>(i, j) = 255;        else if (dst3.at<uchar>(i, j) < 0) dst3.at<uchar>(i, j) = 0;      }    }    vector <Mat> out;    out.push_back(dst1);    out.push_back(dst2);    out.push_back(dst3);    Mat dst;    merge(out, dst);    return dst;  }}
using namespace ACE;
int main() {  Mat src = imread("F:\\sky.jpg");  Mat dst = getACE(src, 4, 7);  imshow("origin", src);  imshow("result", dst);  waitKey(0);}