[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

添加噪声

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;
}

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;
}