USM锐化之openCV实现,附赠调整对比度函数

  常用Photoshop的玩家都知道Unsharp Mask(USM)锐化,它是一种增强图像边缘的锐化算法,原理在此处,如果你想使用这个算法,强烈推荐看一下。本文进行一下简单的介绍,USM锐化一共分为三步,第一步生成原始图片src的模糊图片和高对比度图片,记为blur和contrast.第二,把src和blur作差,得到一张差分图片,记为diff,它就是下图的UnsharpMask。然后把src和contras按一定的比例相加,这个比例由diff控制,最终得到锐化图片。USM有一个缺点,锐化后最大和最小的像素值会超过原始图片,如下图红色虚线和白色实线所示。

 
代码如下:
void MyTreasureBox::UnsharpMask(const IplImage* src, IplImage* dst, float amount, float radius, uchar threshold, int contrast)
{
    if(!src)return ;

    int imagewidth = src->width;
    int imageheight = src->height;
    int channel = src->nChannels;

    IplImage* blurimage =    cvCreateImage(cvSize(imagewidth,imageheight), src->depth, channel);
    IplImage* DiffImage =    cvCreateImage(cvSize(imagewidth,imageheight), 8, channel);

    //原图的高对比度图像
    IplImage* highcontrast = cvCreateImage(cvSize(imagewidth,imageheight), 8, channel);
    AdjustContrast(src, highcontrast, contrast);

    //原图的模糊图像
    cvSmooth(src, blurimage, CV_GAUSSIAN, radius);

    //原图与模糊图作差
    for (int y=0; y<imageheight; y++)
    {
        for (int x=0; x<imagewidth; x++)
        {
            CvScalar ori = cvGet2D(src, y, x);
            CvScalar blur = cvGet2D(blurimage, y, x);
            CvScalar val;
            val.val[0] = abs(ori.val[0] - blur.val[0]);
            val.val[1] = abs(ori.val[1] - blur.val[1]);
            val.val[2] = abs(ori.val[2] - blur.val[2]);

            cvSet2D(DiffImage, y, x, val);
        }
    }

    //锐化
    for (int y=0; y<imageheight; y++)
    {
        for (int x=0; x<imagewidth; x++)
        {
            CvScalar hc = cvGet2D(highcontrast, y, x);
            CvScalar diff = cvGet2D(DiffImage, y, x);
            CvScalar ori = cvGet2D(src, y, x);
            CvScalar val;

            for (int k=0; k<channel; k++)
            {
                if (diff.val[k] > threshold)
                {
                    //最终图像 = 原始*(1-r) + 高对比*r
                    val.val[k] = ori.val[k] *(100-amount) + hc.val[k] *amount;
                    val.val[k] /= 100;
                }
                else
                {
                    val.val[k] = ori.val[k];
                }
            }
            cvSet2D(dst, y, x, val);
        }
    }

    cvReleaseImage(&blurimage);
    cvReleaseImage(&DiffImage);
}

 

其中用到一个调整图像对比度的函数

void MyTreasureBox::AdjustContrast(const IplImage* src, IplImage* dst, int contrast)
{
    if (!src)return ;

    int imagewidth = src->width;
    int imageheight = src->height;
    int channel = src->nChannels;

    //求原图均值
    CvScalar mean = {0,0,0,0};
    for (int y=0; y<imageheight; y++)
    {
        for (int x=0; x<imagewidth; x++)
        {                     
            CvScalar ori = cvGet2D(src, y, x);
            for (int k=0; k<channel; k++)
            {
                mean.val[k] += ori.val[k];
            }         
        }
    }
    for (int k=0; k<channel; k++)
    {
        mean.val[k] /= imagewidth * imageheight;
    }

    //调整对比度
    if (contrast <= -255)    
    {
        //当增量等于-255时,是图像对比度的下端极限,此时,图像RGB各分量都等于阀值,图像呈全灰色,灰度图上只有1条线,即阀值灰度;
        for (int y=0; y<imageheight; y++)
        {
            for (int x=0; x<imagewidth; x++)
            {
                cvSet2D(dst, y, x, mean);
            }
        }
    } 
    else if(contrast > -255 &&  contrast <= 0)
    {
        //(1)nRGB = RGB + (RGB - Threshold) * Contrast / 255
        // 当增量大于-255且小于0时,直接用上面的公式计算图像像素各分量
        //公式中,nRGB表示调整后的R、G、B分量,RGB表示原图R、G、B分量,Threshold为给定的阀值,Contrast为处理过的对比度增量。
        for (int y=0; y<imageheight; y++)
        {
            for (int x=0; x<imagewidth; x++)
            {
                CvScalar nRGB;
                CvScalar ori = cvGet2D(src, y, x);
                for (int k=0; k<channel; k++)
                {
                    nRGB.val[k] = ori.val[k] + (ori.val[k] - mean.val[k]) *contrast /255;
                }
                cvSet2D(dst, y, x, nRGB);
            }
        }
    }
    else if(contrast >0 && contrast <255)
    {
        //当增量大于0且小于255时,则先按下面公式(2)处理增量,然后再按上面公式(1)计算对比度:
        //(2)、nContrast = 255 * 255 / (255 - Contrast) - 255
        //公式中的nContrast为处理后的对比度增量,Contrast为给定的对比度增量。                

        CvScalar nRGB;
        int nContrast = 255 *255 /(255 - contrast) - 255;

        for (int y=0; y<imageheight; y++)
        {
            for (int x=0; x<imagewidth; x++)
            {
                CvScalar ori = cvGet2D(src, y, x);
                for (int k=0; k<channel; k++)
                {
                    nRGB.val[k] = ori.val[k] + (ori.val[k] - mean.val[k]) *nContrast /255;
                }
                cvSet2D(dst, y, x, nRGB);
            }
        }
    }
    else
    {
        //当增量等于 255时,是图像对比度的上端极限,实际等于设置图像阀值,图像由最多八种颜色组成,灰度图上最多8条线,
        //即红、黄、绿、青、蓝、紫及黑与白;        
        for (int y=0; y<imageheight; y++)
        {
            for (int x=0; x<imagewidth; x++)
            {
                CvScalar rgb;
                CvScalar ori = cvGet2D(src, y, x);
                for (int k=0; k<channel; k++)
                {
                    if (ori.val[k] > mean.val[k])
                    {
                        rgb.val[k] = 255;
                    }
                    else
                    {
                        rgb.val[k] = 0;
                    }                    
                }
                cvSet2D(dst, y, x, rgb);
            }
        }
    }
}

 

posted @ 2012-07-03 20:11  汉泽的家  阅读(6967)  评论(2编辑  收藏  举报