图像增强——基于OpenCV的图像色彩增强

前言

这是一个增强图像色彩的demo，基于OpenCV，能很好的增强与锐化图像中的色彩。

色彩增强

1.代码

void colorEnhancement(cv::Mat &src, cv::Mat &dst, int filter)
{

	cv::Mat orig_img = src.clone();
    cv::Mat simg;

	if (orig_img.channels() != 1)
	{
		cvtColor(orig_img, simg, CV_BGR2GRAY);
	}
	else
	{
		return;
	}
    
    long int N = simg.rows*simg.cols;

    int histo_b[256];
    int histo_g[256];
    int histo_r[256];

    for(int i=0; i<256; i++)
	{
        histo_b[i] = 0;
        histo_g[i] = 0;
        histo_r[i] = 0;
    }
    cv::Vec3b intensity;

    for(int i=0; i<simg.rows; i++)
	{
        for(int j=0; j<simg.cols; j++)
		{
            intensity = orig_img.at<cv::Vec3b>(i,j);
           
            histo_b[intensity.val[0]] = histo_b[intensity.val[0]] + 1;
            histo_g[intensity.val[1]] = histo_g[intensity.val[1]] + 1;
            histo_r[intensity.val[2]] = histo_r[intensity.val[2]] + 1;
        }
    }

    for(int i = 1; i<256; i++)
	{
        histo_b[i] = histo_b[i] + filter * histo_b[i-1];
        histo_g[i] = histo_g[i] + filter * histo_g[i-1];
        histo_r[i] = histo_r[i] + filter * histo_r[i-1];
    }

    int vmin_b=0;
    int vmin_g=0;
    int vmin_r=0;
    int s1 = 3;
    int s2 = 3;

    while(histo_b[vmin_b+1] <= N*s1/100)
	{
        vmin_b = vmin_b +1;
    }
    while(histo_g[vmin_g+1] <= N*s1/100)
	{
        vmin_g = vmin_g +1;
    }
    while(histo_r[vmin_r+1] <= N*s1/100)
	{
        vmin_r = vmin_r +1;
    }

    int vmax_b = 255-1;
    int vmax_g = 255-1;
    int vmax_r = 255-1;

    while(histo_b[vmax_b-1]>(N-((N/100)*s2)))
    {   
        vmax_b = vmax_b-1;
    }
    if(vmax_b < 255-1)
	{
        vmax_b = vmax_b+1;
    }
    while(histo_g[vmax_g-1]>(N-((N/100)*s2)))
    {   
        vmax_g = vmax_g-1;
    }
    if(vmax_g < 255-1)
	{
        vmax_g = vmax_g+1;
    }
    while(histo_r[vmax_r-1]>(N-((N/100)*s2)))
    {   
        vmax_r = vmax_r-1;
    }
    if(vmax_r < 255-1)
	{
        vmax_r = vmax_r+1;
    }

    for(int i=0; i<simg.rows; i++)
    {
        for(int j=0; j<simg.cols; j++)
        {
           
            intensity = orig_img.at<cv::Vec3b>(i,j);

            if(intensity.val[0]<vmin_b)
			{
                intensity.val[0] = vmin_b;
            }
            if(intensity.val[0]>vmax_b)
			{
                intensity.val[0]=vmax_b;
            }


            if(intensity.val[1]<vmin_g)
			{
                intensity.val[1] = vmin_g;
            }
            if(intensity.val[1]>vmax_g)
			{
                intensity.val[1]=vmax_g;
            }

            if(intensity.val[2]<vmin_r)
			{
                intensity.val[2] = vmin_r;
            }
            if(intensity.val[2]>vmax_r)
			{
                intensity.val[2]=vmax_r;
            }

			orig_img.at<cv::Vec3b>(i,j) = intensity;
        }
    }

    for(int i=0; i<simg.rows; i++)
	{
        for(int j=0; j<simg.cols; j++)
		{
           
            intensity = orig_img.at<cv::Vec3b>(i,j);
            intensity.val[0] = (intensity.val[0] - vmin_b)*255/(vmax_b-vmin_b);
            intensity.val[1] = (intensity.val[1] - vmin_g)*255/(vmax_g-vmin_g);
            intensity.val[2] = (intensity.val[2] - vmin_r)*255/(vmax_r-vmin_r);
			orig_img.at<cv::Vec3b>(i,j) = intensity;
        }
    }   
    
    
    cv::Mat blurred;
	double sigma = 1;
	double threshold = 50;
	double amount = 1;
    GaussianBlur(orig_img, blurred, cv::Size(), sigma, sigma);
    cv::Mat lowContrastMask = abs(orig_img - blurred) < threshold;
    cv::Mat sharpened = orig_img*(1+amount) + blurred*(-amount);
	orig_img.copyTo(sharpened, lowContrastMask);
	dst = sharpened.clone();
}

2.运行效果