OpenCV实现USM锐化
【转】http://www.programdevelop.com/4964391/
USM (Unsharp masking) is a common operation of image processing. From the Internet search a bit, there are basically three different ways. Only 2 lines of code, there are hundreds of the most complex line. These three methods below summary records for later use.
最简单的方法：
cv::GaussianBlur(frame, image, cv::Size(, ), );
cv::addWeighted(frame, 1.5, image, -0.5, , image);
Followed by the simple method, derived from "only want to hear a good story" programdevelop.com blog.
常用photoshop的一般都会用到usm （unsharp mask）锐化，它的原理非常简单，使用opencv进行实现只需要4行代码
最终实现效果如下：
double sigma = ;
int threshold = ;
float amount = ;
imgsrc = imread("thankyou.jpg");
GaussianBlur(imgsrc, imgblurred, cv::size(0,0), sigma, sigma) #对于图形size（0,0）效果最好。why？看高斯滤波原理
#GaussianBlur(imgsrc, imgblurred, cv::size(5,5), sigma, sigma)
#GaussianBlur(imgsrc, imgblurred, size(), sigma, sigma)
lowcontrastmask = abs(imgsrc-imgblurred)<threshold;
imgdst = imgsrc*(+amount)+imgblurred*(-amount);
imgsrc.copyTo(imgdst, lowcontrastmask);
imshow("SUM", imgdst);
===================================================
GaussianBlur(imgsrc, imgblurred, cv::size(5,5), sigma, sigma)的USM效果
GaussianBlur(imgsrc, imgblurred, cv::size(0,0), sigma, sigma)的USM效果
==================================================
原图像 锐化结果
使用photoshop进行处理的效果如下：
参数：数量131% 半径2.2像素 阈值0色阶
基本上效果还是类似的，通过调节参数可以达到基本一致的效果~~~哈哈
一个简单的usm算法~~~研究了好多天~~~~
不过看到满意的结果还是挺有成就感的
==========原文来自http://www.makaidong.com/%E5%8D%9A%E5%AE%A2%E5%9B%AD%E7%89%9B/4663.shtml====
最复杂的方法：
void UnsharpMask(const IplImage* src, IplImage* dst, float amount=, float radius=, uchar threshold=, intcontrast=)
{
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), , channel); //
IplImage* highcontrast = cvCreateImage(cvSize(imagewidth,imageheight), , channel);
AdjustContrast(src, highcontrast, contrast); //
cvSmooth(src, blurimage, CV_GAUSSIAN, radius); //
for (int y=; y<imageheight; y++)
{
for (int x=; x<imagewidth; x++)
{
CvScalar ori = cvGet2D(src, y, x);
CvScalar blur = cvGet2D(blurimage, y, x);
CvScalar val;
val.val[] = abs(ori.val[] - blur.val[]);
val.val[] = abs(ori.val[] - blur.val[]);
val.val[] = abs(ori.val[] - blur.val[]); cvSet2D(DiffImage, y, x, val);
}
} //
for (int y=; y<imageheight; y++)
{
for (int x=; 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=; k<channel; k++)
{
if (diff.val[k] > threshold)
{
// = *(1-r) + *r
val.val[k] = ori.val[k] *(-amount) + hc.val[k] *amount;
val.val[k] /= ;
}
else
{
val.val[k] = ori.val[k];
}
}
cvSet2D(dst, y, x, val);
}
}
cvReleaseImage(&blurimage);
cvReleaseImage(&DiffImage);
}
//?contrast[-255,255]
void AdjustContrast(const IplImage* src, IplImage* dst, int contrast)
{
if (!src)return ; int imagewidth = src->width;
int imageheight = src->height;
int channel = src->nChannels; //
CvScalar mean = {,,,};
for (int y=; y<imageheight; y++)
{
for (int x=; x<imagewidth; x++)
{
for (int k=; k<channel; k++)
{
CvScalar ori = cvGet2D(src, y, x);
for (int k=; k<channel; k++)
{
mean.val[k] += ori.val[k];
}
}
}
}
for (int k=; k<channel; k++)
{
mean.val[k] /= imagewidth * imageheight;
} //
if (contrast <= -)
{
//-255???RGB??1??
for (int y=; y<imageheight; y++)
{
for (int x=; x<imagewidth; x++)
{
cvSet2D(dst, y, x, mean);
}
}
}
else if(contrast > - && contrast <= )
{
//(1)nRGB = RGB + (RGB - Threshold) * Contrast / 255
// -2550?
//?nRGBR?G?B?RGBR?G?B?Threshold?Contrast?
for (int y=; y<imageheight; y++)
{
for (int x=; x<imagewidth; x++)
{
CvScalar nRGB;
CvScalar ori = cvGet2D(src, y, x);
for (int k=; k<channel; k++)
{
nRGB.val[k] = ori.val[k] + (ori.val[k] - mean.val[k]) *contrast /;
}
cvSet2D(dst, y, x, nRGB);
}
}
}
else if(contrast > && contrast <)
{
//0255?(2)?(1)?
//(2)?nContrast = 255 * 255 / (255 - Contrast) - 255
//nContrast?Contrast? CvScalar nRGB;
int nContrast = * /( - contrast) - ; for (int y=; y<imageheight; y++)
{
for (int x=; x<imagewidth; x++)
{
CvScalar ori = cvGet2D(src, y, x);
for (int k=; k<channel; k++)
{
nRGB.val[k] = ori.val[k] + (ori.val[k] - mean.val[k]) *nContrast /;
}
cvSet2D(dst, y, x, nRGB);
}
}
}
else
{
// 255????8?
//??????
for (int y=; y<imageheight; y++)
{
for (int x=; x<imagewidth; x++)
{
CvScalar rgb;
CvScalar ori = cvGet2D(src, y, x);
for (int k=; k<channel; k++)
{
if (ori.val[k] > mean.val[k])
{
rgb.val[k] = ;
}
else
{
rgb.val[k] = ;
}
}
cvSet2D(dst, y, x, rgb);
}
}
}
}