Retinex SSR算法opencv

#include <cv.h> #include <highgui.h> #include <iostream> #include <math.h> using namespace std; /******************************************************************************** 多尺度Retinex图像增强程序 （一般选用3尺度） src为待处理图像 sigma为高斯模糊标准差 scale为对比度系数 *********************************************************************************/ void MSR(IplImage* src,int sigma_1,int sigma_2,int sigma_3,int scale) { IplImage* src_fl = cvCreateImage(cvGetSize(src),IPL_DEPTH_32F,src->nChannels); IplImage* src_fl1 = cvCreateImage(cvGetSize(src),IPL_DEPTH_32F,src->nChannels); IplImage* src_fl2 = cvCreateImage(cvGetSize(src),IPL_DEPTH_32F,src->nChannels); IplImage* src1_fl = cvCreateImage(cvGetSize(src),IPL_DEPTH_32F,src->nChannels); IplImage* src1_fl1 = cvCreateImage(cvGetSize(src),IPL_DEPTH_32F,src->nChannels); IplImage* src1_fl2 = cvCreateImage(cvGetSize(src),IPL_DEPTH_32F,src->nChannels); IplImage* src2_fl = cvCreateImage(cvGetSize(src),IPL_DEPTH_32F,src->nChannels); IplImage* src2_fl1 = cvCreateImage(cvGetSize(src),IPL_DEPTH_32F,src->nChannels); IplImage* src2_fl2 = cvCreateImage(cvGetSize(src),IPL_DEPTH_32F,src->nChannels); cvConvertScale(src,src_fl,1.0,1.0);//转换范围，所有图像元素增加1.0保证cvlog正常 src1_fl=cvCloneImage(src_fl); src2_fl=cvCloneImage(src_fl); cvLog(src_fl,src_fl1); cvLog(src1_fl,src1_fl1); cvLog(src2_fl,src2_fl1); cvSmooth(src_fl,src_fl2,CV_GAUSSIAN,0,0,sigma_1); //MSR算法的核心之一，高斯模糊 cvSmooth(src1_fl,src1_fl2,CV_GAUSSIAN,0,0,sigma_2); cvSmooth(src2_fl,src2_fl2,CV_GAUSSIAN,0,0,sigma_3); cvLog(src_fl2,src_fl2); cvLog(src1_fl2,src1_fl2); cvLog(src2_fl2,src2_fl2); cvSub(src_fl1,src_fl2,src_fl);//Retinex公式，Log(R(x,y))=Log(I(x,y))-Log(Gauss(I(x,y))) cvSub(src1_fl1,src1_fl2,src1_fl1); cvSub(src2_fl1,src2_fl2,src2_fl1); cvConvertScale(src_fl,src_fl,1.0/3.0,0.0);//每个尺度对应的权值为1/3 cvConvertScale(src1_fl1,src1_fl1,1.0/3.0,0.0); cvConvertScale(src2_fl1,src2_fl1,1.0/3.0,0.0); cvAdd(src_fl,src1_fl1,src1_fl1); cvAdd(src1_fl1,src2_fl1,src2_fl1); //计算图像的均值、方差，MSR算法的核心之二 //使用GIMP中转换方法：使用图像的均值方差等信息进行变换 //没有添加溢出判断 CvScalar mean; CvScalar dev; cvAvgSdv(src2_fl1,&mean,&dev,NULL);//计算图像的均值和标准差 double min[3]; double max[3]; double maxmin[3]; for (int i=0;i<3;i++) { min[i]=mean.val[i]-scale*dev.val[i]; max[i]=mean.val[i]+scale*dev.val[i]; maxmin[i]=max[i]-min[i]; } float* data2=(float*)src2_fl1->imageData; for (int i=0;i<src2_fl1->width;i++) { for(int j=0;j<src2_fl1->height;j++) { data2[j*src2_fl1->widthStep/4+3*i+0]=255*(data2[j*src2_fl1->widthStep/4+3*i+0]-min[0])/maxmin[0]; data2[j*src2_fl1->widthStep/4+3*i+1]=255*(data2[j*src2_fl1->widthStep/4+3*i+1]-min[1])/maxmin[1]; data2[j*src2_fl1->widthStep/4+3*i+2]=255*(data2[j*src2_fl1->widthStep/4+3*i+2]-min[2])/maxmin[2]; } } cvConvertScale(src2_fl1,src,1,0); cvReleaseImage(&src_fl); cvReleaseImage(&src_fl1); cvReleaseImage(&src_fl2); cvReleaseImage(&src1_fl); cvReleaseImage(&src1_fl1); cvReleaseImage(&src1_fl2); cvReleaseImage(&src2_fl); cvReleaseImage(&src2_fl1); cvReleaseImage(&src2_fl2); } void main() { char* filename="C:\\Documents and Settings\\Administrator\\桌面\\待分析的图像\\DSCF0494.bmp"; IplImage* frog=cvLoadImage(filename,1); IplImage* frog1=cvCreateImage(cvGetSize(frog),IPL_DEPTH_32F,frog->nChannels); cvConvertScale(frog,frog1,1.0/255,0); SSR(frog,sigma_1,sigma_2,sigma_3,2); cvNamedWindow("增强后图像"); cvNamedWindow("原始图像"); cvShowImage("原始图像",frog1); cvShowImage("增强后图像",frog); cvSaveImage( "C:/Documents and Settings/Administrator/桌面/待分析的图像/SSR-2D.bmp", frog ); // 并保存结果文件 cvWaitKey(0); cvDestroyAllWindows(); cvReleaseImage(&frog); cvReleaseImage(&frog1); }