一、色彩均衡算法
二、基于参考白的算法

一、色彩均衡算法
1.求出3个通道各个平均值，以及灰度的平均值grayAvg，修改每个原像素值
2.求出像素值最大值maxNum，如果factor=maxNum/255，factor>1,则每个通道像素都除以factor以修改像素值

void Illumination_compensation(V_Image** img)
{
    int col=0,index=0;
    int rows=(*img)->mRow,cols=(*img)->mCol,cns=(*img)->mChannels;
    unsigned char *bArr=(unsigned char *) calloc(rows*cols,sizeof(unsigned char));
    unsigned char *gArr=(unsigned char *) calloc(rows*cols,sizeof(unsigned char));
    unsigned char *rArr=(unsigned char *) calloc(rows*cols,sizeof(unsigned char));
    float bTotal=0,gTotal=0,rTotal=0;
    float bAvg=0.0,gAvg=0.0,rAvg=0.0;
    float bRatio=0,gRatio=0,rRatio=0,grayAvg=0.0;
    //分成三个数组，一个通道一个，并求和
    for(col=0; col<rows*cols*cns; col++)
    {
        bArr[index]=(*img)->mData[col];
        gArr[index]=(*img)->mData[++col];
        rArr[index]=(*img)->mData[++col];
        bTotal+=bArr[index];
        gTotal+=gArr[index];
        rTotal+=rArr[index];
        index++;
    }
    bAvg=bTotal/(rows*cols);
    gAvg=gTotal/(rows*cols);
    rAvg=rTotal/(rows*cols);
    grayAvg=(bAvg+gAvg+rAvg)/3;

    bRatio=grayAvg/bAvg;
    gRatio=grayAvg/gAvg;
    rRatio=grayAvg/rAvg;

    int maxNum=0;
    for(index=0; index<rows*cols; index++)
    {
        bArr[index]=floor(bArr[index]*bRatio);
        gArr[index]=floor(gArr[index]*gRatio);
        rArr[index]=floor(rArr[index]*rRatio);

        if( bArr[index]>maxNum)
            maxNum=bArr[index];
        if( gArr[index]>maxNum)
            maxNum=gArr[index];
        if( rArr[index]>maxNum)
            maxNum=rArr[index];
    }

    int factor=maxNum/255,blue,green,red;
    index=0;

    for(col=0; col<cns*rows*cols; col++)
    {
        if(factor>1)
        {
            blue=bArr[index]/factor;
            green=gArr[index]/factor;
            red=rArr[index]/factor;
            index++;
        }
        else
        {
            blue=bArr[index];
            green=gArr[index];
            red=rArr[index];
            index++;
        }
        (*img)->mData[col]=detectNum(blue);
        (*img)->mData[++col]=detectNum(green);
        (*img)->mData[++col]=detectNum(red);

    }
}

二、基于参考白的算法
1.统计每个灰度值的像素个数
2.求出值前大5%的像素灰度像素最为参考白，然后可以获得参考白的平均灰度值avgGray=参考白总像素值totalPixel/参考白像素个数total。则光照

void Illumination_compensation(V_Image** img)
{
    int col=0,index=0;
    int rows=(*img)->mRow,cols=(*img)->mCol,cns=(*img)->mChannels;
    unsigned char *grayArr=(unsigned char *) calloc(rows*cols,sizeof(unsigned char));
    ///计算原像素的灰度值
    for(col=0; col<rows*cols*cns; col++)
        grayArr[index++]=((*img)->mData[col] * 0.114 + (*img)->mData[++col] * 0.587 + (*img)->mData[++col]  * 0.299);

    int num=floor(rows*cols*0.05);
    int *refIndexArr=(int *) calloc(num,sizeof(int));//参考白数列
    int *avgRefIndexArr=(int *) calloc(num,sizeof(int));//参考白数列
    float *pixelNum=(float *) calloc(256,sizeof(float));//像素数数列
    ///初始化数组为0
    for(index=0; index<256; index++)
        pixelNum[index]=0;

    ///统计像素灰度个数
    for(col=0; col<rows*cols; col++ )
        pixelNum[grayArr[col]]++;

    ///求出灰度值从大到小前百分之五的灰度值是哪个灰度值maxPixel
    int total=0,maxPixel;
    for(index=255; index>=0; index--)
    {
        if(total<num)
        {
            total+=pixelNum[index];
            maxPixel=index;
        }
        else break;
    }
    ///求出参考白的总灰度数
    int totalPixel=0;
    for(index=maxPixel; index<255; index++)
        totalPixel+=index*pixelNum[index];
    ///参考白的平均灰度数
    float avgGray=totalPixel/total;
    ///光照补偿系数
    float coe=255/avgGray;
    ///原像素值乘以光照补偿系数
    for(index=0; index<rows*cols; index++)
    {
        (*img)->mData[index*3]=detectNum((*img)->mData[index*3]*coe);
        (*img)->mData[index*3+1]=detectNum((*img)->mData[index*3+1]*coe);
        (*img)->mData[index*3+2]=detectNum((*img)->mData[index*3+2]*coe);
    }
}