双边过滤器

时间:2011-04-17 19:26:21

标签: algorithm vision

如果使用高斯滤波器,如何实现双边滤波器?

1 个答案:

答案 0 :(得分:10)

简单的双边滤波器可以定义为

Inew(x,y)=求和(j = yn / 2; j&lt; = y + n / 2)求和(i = xm / 2; j <= x + m / 2)w(i,j,的x,y)I(I,J)

其中常见的低通滤波器,如高斯滤波器,具有权重w(i,j,x,y),基于从内核中心(x,y)到每个像素的距离(i, j)的。对于双边滤波器,权重基于两个距离确定:图像空间距离和颜色空间距离。

下面是一个简单的C实现

void convolution(uchar4 *_in, uchar4 *_out, int width, int height, int ~ halfkernelsize, float id, float cd)
{ 
  int kernelDim = 2*halfkernelsize+1;
for(int y=0; y float sumWeight = 0; unsigned int ctrIdx = y*width + x; float ctrPix[3]; ctrPix[0] = _in[ctrIdx].x; ctrPix[1] = _in[ctrIdx].y; ctrPix[2] = _in[ctrIdx].z; // neighborhood of current pixel int kernelStartX, kernelEndX, kernelStartY, kernelEndY; kernelStartX = x-halfkernelsize; kernelEndX = x+halfkernelsize; kernelStartY = y-halfkernelsize; kernelEndY = y+halfkernelsize; for(int j= kernelStartY; j<= kernelEndY; j++) { for(int i= kernelStartX; i<= kernelEndX; i++) { unsigned int idx = max(0, min(j, height-1))*width + max(0, min(i,width-1)); float curPix[3]; curPix[0] = _in[idx].x; curPix[1] = _in[idx].y; curPix[2] = _in[idx].z; float currWeight; // define bilateral filter kernel weights float imageDist = sqrt( (float)((i-x)*(i-x) + (j-y)*(j-y)) ); float colorDist = sqrt( (float)( (curPix[0] - ctrPix[0])*(curPix[0] - ctrPix[0]) + (curPix[1] - ctrPix[1])*(curPix[1] - ctrPix[1]) + (curPix[2] - ctrPix[2])*(curPix[2] - ctrPix[2]) ) ); currWeight = 1.0f/(exp((imageDist/id)*(imageDist/id)*0.5)*exp((colorDist/cd)*(colorDist/cd)*0.5)); sumWeight += currWeight; _sum[0] += currWeight*curPix[0]; _sum[1] += currWeight*curPix[1]; _sum[2] += currWeight*curPix[2]; } } _sum[0] /= sumWeight; _sum[1] /= sumWeight; _sum[2] /= sumWeight; _out[ctrIdx].x = (int)(floor(_sum[0])); _out[ctrIdx].y = (int)(floor(_sum[1])); _out[ctrIdx].z = (int)(floor(_sum[2])); _out[ctrIdx].w = _in[ctrIdx].w; } }

}