双边过滤算法

  1 /*对整张图片进行双边平滑处理就是去掉噪音，模糊图像*/
  2 
  3 /*
  4 *  buff        ->图片数据资源
  5 *  desbuff     ->处理之后的图片数据资源
  6 *  width       -> 图片宽度 
  7 *  height       -> 图片高度
  8 *  window_size -> 滑动窗口大小
  9 */
 10 
 11 double distance(double x1 ,double x2,double y1, double y2  ){
 12 
 13     return ((x1 -x2)*(x1 -x2) + (y1-y2)*(y1-y2));
 14 }
 15 
 16 
 17 void BiLateralFilter(wu_char* buff , wu_char* desbuff, 
 18     unsigned int width ,unsigned int height ,
 19     double cgmal , double sgmal ,int window_size 
 20     )
 21 {
 22 
 23     double * xydis = new double[window_size*window_size];   //各个像素点到中心像素点之间几何距离
 24     double * Rdis  = new double[window_size*window_size];   //RGB图像中R色素到中心R颜色的分量差
 25     double * Gdis  = new double[window_size*window_size];   //RGB图像中G色素到中心G颜色的分量差
 26     double * Bdis  = new double[window_size*window_size];   //RGB图像中B色素到中心B颜色的分量差
 27     int xpos, ypos ,i,j,k;
 28 
 29     double midxy =(window_size-1.0)/2;
 30 
 31     for(int i=0 ; i<window_size*window_size ; i++){
 32         
 33         xpos  = i/window_size;
 34         ypos  = i%window_size;
 35         xydis[i] = distance(xpos,midxy ,ypos ,midxy);
 36         xydis[i] = exp(-0.5*xydis[i]/cgmal/cgmal) ;  /*丁一个bug*/
 37     }
 38 
 39     double rvar,gvar,bvar;
 40     int nX,nY;
 41     for( i=1; i<2 ; i++){
 42         for( j=0;j<width ; j++){
 43             //RGB为基本单元,获取当前（x,y）坐标的（RGB单元）
 44             rvar = (int)buff[i][j*3];
 45             gvar = (int)buff[i][j*3+1];
 46             bvar = (int)buff[i][j*3+2];
 47 
 48             double  rdata =0,gdata=0,bdata=0;
 49             double  rtotal =0,gtotal=0,btotal=0; //平均均方根
 50 
 51             for(k=0; k<window_size*window_size  ; k++){
 52                 
 53                 ypos = k/window_size;
 54                 xpos = k%window_size;
 55               
 56                 nY= i - midxy+ypos;
 57                 nX= j - midxy+xpos;
 58                 
 59                 if((nY>=0)&&(nY<height)&&(nX>=0)&&(nX<width)){
 60                 
 61                 //防止越界
 62                  double rvar1 = (int)buff[nY][nX*3];
 63                   double gvar1 = (int)buff[nY][nX*3+1];
 64                  double bvar1 = (int)buff[nY][nX*3+2];
 65                 
 66                  //对颜色分量进行处理
 67                  Rdis[k] = fabs(rvar-rvar1);
 68                  Rdis[k] = exp(-0.5*Rdis[k]*Rdis[k]/sgmal/sgmal);
 69 
 70                  Gdis[k] = fabs(gvar-gvar1);
 71                  Gdis[k] = exp(-0.5*Gdis[k]*Gdis[k]/sgmal/sgmal);
 72 
 73                  Bdis[k] = fabs(bvar-bvar1);
 74                  Bdis[k] = exp(-0.5*Bdis[k]*Bdis[k]/sgmal/sgmal);
 75                 
 76                  if(k!=4){
 77                      rdata +=  rvar1*Rdis[k]*xydis[k]; //c、s参数综合
 78                      rtotal += Rdis[k]*xydis[k]; //加权系数求和，并进行归一化
 79                     
 80                      gdata +=  gvar1*Gdis[k]*xydis[k]; //c、s参数综合
 81                      gtotal += Gdis[k]*xydis[k]; //加权系数求和，并进行归一化
 82                      
 83                      bdata +=  bvar1*Bdis[k]*xydis[k]; //c、s参数综合
 84                      btotal += Bdis[k]*xydis[k]; //加权系数求和，并进行归一化
 85                  }
 86                 }
 87             }
 88                rdata/=rtotal;
 89                gdata/=gtotal;
 90                bdata/=btotal;
 91                desbuff[i][j*3]=dealOver((int)(rdata));
 92                desbuff[i][j*3+1]=dealOver((int)(gdata));
 93                desbuff[i][j*3+2]=dealOver((int)(bdata));
 94         }
 95     }
 96 
 97     delete [] xydis ;
 98     delete [] Rdis  ; 
 99     delete [] Gdis  ;
100     delete [] Bdis  ;
101 }
102 
103 
104 /*此处因为不是压缩图片，不需要进行DCT离散余弦卷积化,
105     单纯的对16个数据(4*4)数据进行中和之后超过255进行处理*/
106 
107 unsigned int dealOver(long color){
108 
109     if(color<0)return 0;            /*黑色*/
110     if(color>255) return 255;        /*无色*/
111     /*0-255属于合理范围*/
112     return color;
113 }