ISP基礎（10）-Gamma校正及其實現

圖2中左圖為原圖，中圖為gamma = 1/2.2在校正結果，原圖中左半側的灰度值較高，右半側的灰度值較低，經過gamma = 1/2.2校正后(中圖)，左側的對比度降低(見胡須)，右側在對比度提高(明顯可以看清面容)，同時圖像在的整體灰度值提高。

右圖為gamma = 2.2在校正結果，校正后，左側的對比度提高(見胡須)，右側在對比度降低(面容更不清楚了)，同時圖像在的整體灰度值降低。

值得一提的是，人眼是按照gamma < 1的曲線對輸入圖像進行處理的。

參考資料：

[1] http://en.wikipedia.org/wiki/Gamma_correction

[2] http://www.cambridgeincolour.com/tutorials/gamma-correction.htm

[3] https://github.com/bytefish/opencv/blob/master/misc/tan_triggs.cpp

代碼:

#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
 
#include <iostream>
 
using namespace cv;
using namespace std;
 
// Normalizes a given image into a value range between 0 and 255.
Mat norm_0_255(const Mat& src) {
    // Create and return normalized image:
    Mat dst;
    switch(src.channels()) {
    case 1:
        cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
        break;
    case 3:
        cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
        break;
    default:
        src.copyTo(dst);
        break;
    }
    return dst;
}
 
int main(int argc, const char *argv[]) {
    // Get filename to the source image:
    if (argc != 2) {
        cout << "usage: " << argv[0] << " <image.ext>" << endl;
        exit(1);
    }
    // Load image & get skin proportions:
    //Mat image = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
	Mat image = imread(argv[1], CV_LOAD_IMAGE_COLOR);
    // Convert to floating point:
    Mat X;
	image.convertTo(X, CV_32FC1);
	//image.convertTo(X, CV_32F);
    // Start preprocessing:
    Mat I;
	float gamma = 1/2.2;
    pow(X, gamma, I);
 
 
    // Draw it on screen:
    imshow("Original Image", image);
    imshow("Gamma correction image", norm_0_255(I));
	//imwrite("origin.jpg", image);
	imwrite("gamma_inv2.2.jpg", norm_0_255(I));
    // Show the images:
    waitKey(0);
    // Success!
    return 0;
}