msrcr图像增强算法opencv 实现

MSRCR（Multi-Scale Retinex with Color Restoration）是一种用于图像增强的算法，可以使图像的亮度、对比度和饱和度得到更好的改善。下面是使用OpenCV实现MSRCR算法的步骤：

1. 读取图像并转换为Lab色彩空间。

Mat image = imread("image.jpg");
Mat labImage;
cvtColor(image, labImage, COLOR_BGR2Lab);

2. 对Lab图像进行多尺度Retinex处理。

Mat msrImage;
vector<Mat> labChannels;
split(labImage, labChannels);
for (int i = 0; i < labImage.channels(); i++) {
    Mat channel = labChannels[i];
    Mat logChannel;
    log(channel + 1, logChannel);
    for (int sigma = 1; sigma < 10; sigma += 2) {
        Mat blurred;
        GaussianBlur(logChannel, blurred, Size(sigma, sigma), 0);
        Mat diff;
        subtract(logChannel, blurred, diff);
        Mat scaled;
        divide(diff, (sigma * sigma), scaled);
        add(msrImage, scaled, msrImage);
    }
}

3. 对多尺度Retinex处理后的图像进行颜色恢复。

Mat restoredImage;
for (int i = 0; i < labImage.channels(); i++) {
    Mat channel = labChannels[i];
    Mat expChannel;
    exp(msrImage - Scalar::all(1), expChannel);
    Mat restoredChannel;
    divide(channel, expChannel, restoredChannel);
    restoredImage.push_back(restoredChannel);
}
Mat enhancedImage;
cvtColor(restoredImage, enhancedImage, COLOR_Lab2BGR);

4. 显示增强后的图像。

imshow("Enhanced Image", enhancedImage);
waitKey(0);

完整代码如下：

#include <opencv2/opencv.hpp>

using namespace cv;
using namespace std;

int main() {
    Mat image = imread("image.jpg");
    Mat labImage;
    cvtColor(image, labImage, COLOR_BGR2Lab);

    Mat msrImage = Mat::zeros(labImage.size(), CV_32F);
    vector<Mat> labChannels;
    split(labImage, labChannels);
    for (int i = 0; i < labImage.channels(); i++) {
        Mat channel = labChannels[i];
        Mat logChannel;
        log(channel + 1, logChannel);
        for (int sigma = 1; sigma < 10; sigma += 2) {
            Mat blurred;
            GaussianBlur(logChannel, blurred, Size(sigma, sigma), 0);
            Mat diff;
            subtract(logChannel, blurred, diff);
            Mat scaled;
            divide(diff, (sigma * sigma), scaled);
            add(msrImage, scaled, msrImage);
        }
    }

    Mat restoredImage = Mat::zeros(labImage.size(), CV_32F);
    for (int i = 0; i < labImage.channels(); i++) {
        Mat channel = labChannels[i];
        Mat expChannel;
        exp(msrImage - Scalar::all(1), expChannel);
        Mat restoredChannel;
        divide(channel, expChannel, restoredChannel);
        restoredImage.push_back(restoredChannel);
    }
    Mat enhancedImage;
    cvtColor(restoredImage, enhancedImage, COLOR_Lab2BGR);

    imshow("Enhanced Image", enhancedImage);
    waitKey(0);
    return 0;
}