公式：

两个参数 \alpha > 0 和 \beta 一般称作 增益 和 偏置 参数。我们往往用这两个参数来分别控制 对比度 和 亮度 。

1. #include "stdafx.h"
2. #include<iostream>
3. #include<thread>
4. #include<vector>
5. #include <opencv2/core/core.hpp>
6. #include <opencv2/contrib/contrib.hpp>
7. #include <opencv2/highgui/highgui.hpp>
8. #include <opencv2/imgproc/imgproc.hpp>
9. #include <opencv2/objdetect/objdetect.hpp>
10.  
11. using
    namespace cv;
12. using
    namespace std;
13.  
14. int g_slider_position = 0, g_slider_position2 = 0;
15. Mat image;
16. Mat new_image;
17. double alpha, beta;
18.  
19. void onTrackingbarSlide(int pos)
20. {
21.    new_image = Mat::zeros(image.size(), image.type());
22.    beta = pos;
23.    for (int y = 0; y < image.rows; y++)
24.    {
25.       for (int x = 0; x < image.cols; x++)
26.       {
27.          for (int c = 0; c < 3; c++)
28.          {
29.             //saturate_cast 防止数据溢出
30.             new_image.at<Vec3b>(y, x)[c] = saturate_cast<uchar>(alpha*(image.at<Vec3b>(y, x)[c]) + beta);
31.          }
32.       }
33.    }
34.  
35.    imshow("New Image", new_image);
36. }
37.  
38. void onTrackingbarSlide2(int pos)
39. {
40.    new_image = Mat::zeros(image.size(), image.type());
41.  
42.    alpha = (double)pos / 10.0;
43.  
44.    for (int y = 0; y < image.rows; y++)
45.    {
46.       for (int x = 0; x < image.cols; x++)
47.       {
48.          for (int c = 0; c < 3; c++)
49.          {
50.             //saturate_cast 防止数据溢出
51.             new_image.at<Vec3b>(y, x)[c] = saturate_cast<uchar>(alpha*(image.at<Vec3b>(y, x)[c]) + beta);
52.          }
53.       }
54.    }
55.  
56.    imshow("New Image", new_image);
57. }
58.  
59. int _tmain(int argc, _TCHAR* argv[])
60. {
61.    /// 读入用户提供的图像
62.    image = imread("E:\\myImage\\sql.png");
63.  
64.    //初始化为0的数组
65.    Mat new_image = Mat::zeros(image.size(), image.type());
66.  
67.    /// 初始化
68.    cout << "* Enter the alpha value [1.0-3.0]: ";
69.    cin >> alpha;
70.    cout << "* Enter the beta value [0-100]: ";
71.    cin >> beta;
72.  
73.    /// 创建窗口
74.    namedWindow("Original Image", 1); // 1:WINDOW_AUTOSIZE
75.    namedWindow("New Image", 1);
76.  
77.    cvCreateTrackbar("亮度（增益）", "New Image", &g_slider_position, 100, onTrackingbarSlide);
78.    cvCreateTrackbar("对比度（偏置）", "New Image", &g_slider_position2, 30, onTrackingbarSlide2);
79.  
80.    /// 执行运算 new_image(i,j) = alpha*image(i,j) + beta
81.    for (int y = 0; y < image.rows; y++)
82.    {
83.       for (int x = 0; x < image.cols; x++)
84.       {
85.          for (int c = 0; c < 3; c++)
86.          {
87.             //saturate_cast 防止数据溢出
88.             new_image.at<Vec3b>(y, x)[c] = saturate_cast<uchar>(alpha*(image.at<Vec3b>(y, x)[c]) + beta);
89.          }
90.       }
91.    }
92.  
93.    /// 显示图像
94.    imshow("Original Image", image);
95.    imshow("New Image", new_image);
96.  
97.    /// 等待用户按键
98.    waitKey();
99.    return 0;
100. }

参考：

http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/core/basic_linear_transform/basic_linear_transform.html#basic-linear-transform

http://blog.csdn.net/mjlsuccess/article/details/12401839