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全局亮度调整:

$I_{m}$，输入图像平均亮度：
$I_{m} = e{^{(\dfrac{1}{M*N}\sum_{x=1}^M \sum_{y=1}^N log(I(x,y)+1))}} - 1$

//亮度均值 exp(x)void CImagesQualityEvaluationToolDlg::OnBnClickedButton4(){    /*if((image_in_1.rows != image_in_2.rows)||(image_in_1.cols != image_in_2.cols)        ||(image_in_1.rows != image_in_2.rows)||(image_in_1.cols != image_in_2.cols))    {        CString str_Problem("图片1和2大小不一致");        GetDlgItem(IDC_EDIT1)->SetWindowText(str_Problem);        return;    }*/    //unsigned char***texture = NULL;    //texture=ZL_allocu_3(image_in_1.rows,image_in_1.cols,3);       //allocate memory    double Img_b=0.,Img_g=0.,Img_r=0.;    double Img_bgr=0.,PSNR_b=0.,PSNR_g=0.,PSNR_r=0.,PSNR=0.;    for (int row = 0; row < image_in_1.rows; row++)    {        for (int col = 0; col < image_in_1.cols; col++)        {            Img_b += log((double)(*(image_in_1.data + image_in_1.step[0] * row + image_in_1.step[1] * col)) + 1.);            Img_g += log((double)(*(image_in_1.data + image_in_1.step[0] * row + image_in_1.step[1] * col + image_in_1.elemSize1()))  + 1.);            Img_r += log((double)(*(image_in_1.data + image_in_1.step[0] * row + image_in_1.step[1] * col + image_in_1.elemSize1() * 2))  + 1.);            }    }    int WH = image_in_1.rows * image_in_1.cols;    Img_b = Img_b/WH;    Img_g = Img_g/WH;    Img_r = Img_r/WH;    Img_b = exp(Img_b) - 1;    Img_g = exp(Img_g) - 1;    Img_r = exp(Img_r) - 1;    Img_bgr = (Img_b+Img_g+Img_r)/3;    CString str_Img_bgr;    str_Img_bgr.Format("%f",Img_bgr);    GetDlgItem(IDC_EDIT5)->SetWindowText(str_Img_bgr); }

s，缩放因子
$s = k*I_{m} + b$
k和b取值，代入
$s = -4.3*10^{-5}*I_{m} + 0.018$

$I_{\omega}$，白点参数
$I_{\omega} = s * Min(255,Ratio(I,0.8)*1.25)$
$I_{\omega} = （-4.3*10^{-5}*I_{m} + 0.018） * Min(255,Ratio(I,0.8)*1.25)$

$I_{s}(x,y) = s *I(x,y)$

$I^{'} = \dfrac{I_{s}}{1+I_{s}^{\gamma}}*(I_{\omega}^{\gamma-1}+\dfrac{I_{s}}{I_{\omega}^{2}})$

$I^{'} = \dfrac{ I*s*(I_{\omega}^{\gamma-1}+\dfrac{s*I}{I_{\omega}^{2}})}{1+(s*I)^{\gamma}}$

全局调整因子：
$f_{0}(I) = \dfrac{ s*(I_{\omega}^{\gamma-1}+\dfrac{s*I}{I_{\omega}^{2}})}{1+(s*I)^{\gamma}}$

$I_{abf}， I$的自适应双边滤波结果
$f_{0}(I) = \dfrac{ s*(I_{\omega}^{\gamma-1}+\dfrac{s*I_{abf}}{I_{\omega}^{2}})}{1+(s*I_{abf})^{\gamma}}$

$f_{0}(I) = \dfrac{ （-4.3*10^{-5}*I_{m} + 0.018）*(I_{\omega}^{\gamma-1}+\dfrac{（-4.3*10^{-5}*I_{m} + 0.018）*I_{abf}}{I_{\omega}^{2}})}{1+(（-4.3*10^{-5}*I_{m} + 0.018）*I_{abf})^{\gamma}}$

参考文献:
1、《图像亮度的自动调整》
2、《图像和视频亮度的自动调整》 王 想1,郭延文13,杜振龙2,3,武港山1,张福炎1,彭群生2
(1.南京大学计算机软件新技术国家重点实验室,江苏南京210093;2.浙江大学CAD&CG国家重点实验室,浙江杭州310058;3.南京工业大学信息科学与工程学院,江苏南京210009)