[转]图像去噪的OpenCV添加噪声和去噪

图像去噪的OPenCV添加噪声和去噪

原文地址：http://blog.csdn.net/margaret_wangrui/article/details/51939153

2016-07-18

版权声明：本文为博主原创文章，未经博主允许不得转载。

 

目录(?)[+]

 

添加噪声

1. 添加高斯噪声

IplImage* AddGuassianNoise(IplImage* src)    //添加高斯噪声 { IplImage* dst = cvCreateImage(cvGetSize(src),src->depth,src->nChannels); IplImage* noise = cvCreateImage(cvGetSize(src),src->depth,src->nChannels); CvRNG rng = cvRNG(-1); cvRandArr(&rng,noise,CV_RAND_NORMAL,cvScalarAll(0),cvScalarAll(25)); cvAdd(src,noise,dst); return dst; }

1. 添加椒盐噪声

IplImage* AddPepperSaltNoise(IplImage* src)    //添加椒盐噪声，随机黑白点
{
    IplImage* dst = cvCreateImage(cvGetSize(src),src->depth,src->nChannels);
    cvCopy(src, dst);
    for(int k=0; k<(src->height*src->width*P); k++) { int i = rand()%src->height; int j = rand()%src->width; int m = rand()%2; CvScalar s = cvGet2D(src, i, j); if(src->nChannels == 1) { if(m==0) { s.val[0] = 255; } else { s.val[0] = 0; } } else if(src->nChannels==3) { if(m==0) { s.val[0]=255; s.val[1]=255; s.val[2]=255; } else { s.val[0]=0; s.val[1]=0; s.val[2]=0; } } cvSet2D(dst, i, j, s); } return dst; }

 

实现滤波器

1、 算术均值滤波器

//算术均值滤波器——模板大小5*5 IplImage* ArithmeticMeanFilter(IplImage* src) { IplImage* dst = cvCreateImage(cvGetSize(src),src->depth,src->nChannels); cvSmooth(src,dst,CV_BLUR,5); return dst; }

 

2、 几何均值滤波器

//几何均值滤波器——模板大小5*5 IplImage* GeometryMeanFilter(IplImage* src) { IplImage* dst = cvCreateImage(cvGetSize(src),src->depth,src->nChannels); int row, col; int h=src->height; int w=src->width; double mul[3]; double dc[3]; int mn; //计算每个像素的去噪后color值 for(int i=0;i<src->height;i++){ for(int j=0;j<src->width;j++){ mul[0]=1.0; mn=0; //统计邻域内的几何平均值，邻域大小5*5 for(int m=-2;m<=2;m++){ row = i+m; for(int n=-2;n<=2;n++){ col = j+n; if(row>=0&&row<h && col>=0 && col<w){ CvScalar s = cvGet2D(src, row, col); mul[0] = mul[0]*(s.val[0]==0?1:s.val[0]); //邻域内的非零像素点相乘 mn++; } } } //计算1/mn次方 CvScalar d; dc[0] = pow(mul[0], 1.0/mn); d.val[0]=dc[0]; //统计成功赋给去噪后图像。 cvSet2D(dst, i, j, d); } } return dst; }

3、谐波均值滤波器

//谐波均值滤波器——模板大小5*5 IplImage* HarmonicMeanFilter(IplImage* src) { IplImage* dst = cvCreateImage(cvGetSize(src),src->depth,src->nChannels); int row, col; int h=src->height; int w=src->width; double sum[3]; double dc[3]; int mn; //计算每个像素的去噪后color值 for(int i=0;i<src->height;i++){ for(int j=0;j<src->width;j++){ sum[0]=0.0; mn=0; //统计邻域,5*5模板 for(int m=-2;m<=2;m++){ row = i+m; for(int n=-2;n<=2;n++){ col = j+n; if(row>=0 && row<h && col>=0 && col<w){ CvScalar s = cvGet2D(src, row, col); sum[0] = sum[0]+(s.val[0]==0?255:255/s.val[0]); mn++; } } } CvScalar d; dc[0] = mn*255/sum[0]; d.val[0]=dc[0]; //统计成功赋给去噪后图像。 cvSet2D(dst, i, j, d); } } return dst; }

4、逆谐波均值滤波器

//逆谐波均值大小滤波器——模板大小5*5 IplImage* InverseHarmonicMeanFilter(IplImage* src) { IplImage* dst = cvCreateImage(cvGetSize(src),src->depth,src->nChannels); //cvSmooth(src,dst,CV_BLUR,5); int row, col; int h=src->height; int w=src->width; double sum[3]; double sum1[3]; double dc[3]; double Q=2; //计算每个像素的去噪后color值 for(int i=0;i<src->height;i++){ for(int j=0;j<src->width;j++){ sum[0]=0.0; sum1[0]=0.0; //统计邻域 for(int m=-2;m<=2;m++){ row = i+m; for(int n=-2;n<=2;n++){ col = j+n; if(row>=0&&row<h && col>=0 && col<w){ CvScalar s = cvGet2D(src, row, col); sum[0] = sum[0]+pow(s.val[0]/255, Q+1); sum1[0] = sum1[0]+pow(s.val[0]/255, Q); } } } //计算1/mn次方 CvScalar d; dc[0] = (sum1[0]==0?0:(sum[0]/sum1[0]))*255; d.val[0]=dc[0]; //统计成功赋给去噪后图像。 cvSet2D(dst, i, j, d); } } return dst; }