OpenCV4入门教程090：强度变换

图像强度

英文名称是image intensity，意思是单通道图像像素的值大小。在灰度图像中，图像强度是就是图像的灰度级。在RGB颜色空间中，可以理解为RGB三个通道的像素灰度值，即RGB包含三种图像强度。其他颜色空间也是同样的道理。

测试代码：

#include "opencv2/core.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/intensity_transform.hpp"

#include <iostream>

using namespace std;
using namespace cv;
using namespace cv::intensity_transform;

namespace {
// global variables
Mat g_image;

int               g_gamma    = 40;
const int         g_gammaMax = 500;
Mat               g_imgGamma;
const std::string g_gammaWinName = "Gamma Correction";

Mat               g_contrastStretch;
int               g_r1              = 70;
int               g_s1              = 15;
int               g_r2              = 120;
int               g_s2              = 240;
const std::string g_contrastWinName = "Contrast Stretching";

Mat               g_imgBIMEF;
int               g_mu           = 50;
const int         g_muMax        = 100;
const std::string g_BIMEFWinName = "BIMEF";

static void onTrackbarGamma(int, void *) {
    float gamma = g_gamma / 100.0f;
    gammaCorrection(g_image, g_imgGamma, gamma);
    imshow(g_gammaWinName, g_imgGamma);
}

static void onTrackbarContrastR1(int, void *) {
    contrastStretching(g_image, g_contrastStretch, g_r1, g_s1, g_r2, g_s2);
    imshow("Contrast Stretching", g_contrastStretch);
}

static void onTrackbarContrastS1(int, void *) {
    contrastStretching(g_image, g_contrastStretch, g_r1, g_s1, g_r2, g_s2);
    imshow("Contrast Stretching", g_contrastStretch);
}

static void onTrackbarContrastR2(int, void *) {
    contrastStretching(g_image, g_contrastStretch, g_r1, g_s1, g_r2, g_s2);
    imshow("Contrast Stretching", g_contrastStretch);
}

static void onTrackbarContrastS2(int, void *) {
    contrastStretching(g_image, g_contrastStretch, g_r1, g_s1, g_r2, g_s2);
    imshow("Contrast Stretching", g_contrastStretch);
}

static void onTrackbarBIMEF(int, void *) {
    float mu = g_mu / 100.0f;
    BIMEF(g_image, g_imgBIMEF, mu);
    imshow(g_BIMEFWinName, g_imgBIMEF);
}
} // namespace

int main(int argc, char **argv) {
    // Read input image
    g_image = imread("1.png");
    if(g_image.empty()) {
        std::cout<<"Image is empty!"<<std::endl;
    }

    // Create trackbars
    namedWindow(g_gammaWinName);
    createTrackbar("Gamma value", g_gammaWinName, &g_gamma, g_gammaMax, onTrackbarGamma);

    namedWindow(g_contrastWinName);
    createTrackbar("Contrast R1", g_contrastWinName, &g_r1, 256, onTrackbarContrastR1);
    createTrackbar("Contrast S1", g_contrastWinName, &g_s1, 256, onTrackbarContrastS1);
    createTrackbar("Contrast R2", g_contrastWinName, &g_r2, 256, onTrackbarContrastR2);
    createTrackbar("Contrast S2", g_contrastWinName, &g_s2, 256, onTrackbarContrastS2);

    namedWindow(g_BIMEFWinName);
    createTrackbar("Enhancement ratio mu", g_BIMEFWinName, &g_mu, g_muMax, onTrackbarBIMEF);

    // Apply intensity transformations
    Mat imgAutoscaled, imgLog;
    autoscaling(g_image, imgAutoscaled);
    gammaCorrection(g_image, g_imgGamma, g_gamma / 100.0f);
    logTransform(g_image, imgLog);
    contrastStretching(g_image, g_contrastStretch, g_r1, g_s1, g_r2, g_s2);
    BIMEF(g_image, g_imgBIMEF, g_mu / 100.0f);

    // Display intensity transformation results
    imshow("Original Image", g_image);
    imshow("Autoscale", imgAutoscaled);
    imshow(g_gammaWinName, g_imgGamma);
    imshow("Log Transformation", imgLog);
    imshow(g_contrastWinName, g_contrastStretch);
    imshow(g_BIMEFWinName, g_imgBIMEF);

    waitKey(0);
    return 0;
}

流程为

使用autoscaling函数将图片的值缩放到[0,255]范围内，用以增强图片对比度，这就是自动变换
gamma变换
对数变换
对比度拉伸
生物启发的低光图像增强多曝光融合框架（A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement,BIMEF）。低光图像由于可见性低，不利于人类观测和计算机视觉算法。虽然许多人提出了许多图像增强技术来解决这一问题，但现有的方法不可避免地会引入对比度欠增强和过增强。受人类视觉系统的启发，我们设计了一个用于弱光图像增强的多曝光融合框架。在此框架上，我们提出了一种双曝光融合算法，以提供准确的对比度和亮度增强。具体地说，我们首先利用照明估计技术设计了图像融合的权重矩阵。然后，我们引入了我们的相机响应模型来合成多曝光图像。接下来，我们找到最佳的曝光率，以便合成图像在原始图像曝光不足的区域被很好地曝光。最后，根据权重矩阵将输入图像与合成图像进行融合，得到了增强效果。实验表明，与几种最先进的方法相比，我们的方法可以获得较少的对比度和光失真的结果。

效果为：

result