图像保边滤波算法集锦--EPM滤波算法与实现
EPM滤波即Edge preserved mean filter，算法本身来自于一篇公开专利，专利号为CN102509266：一种图像快速保边滤波方法；
该专利设计了一种快速保边滤波器，可以达到双边滤波的效果，而时间复杂度为O(l)，实际上该算法也是基于局部均值方差信息，来进行边缘保留的。个人觉得算法介于局部均值滤波和导向滤波之间，有类似之处，大家可以对比这三种算法以做深入了解。
算法流程如下：
整个算法计算了一次均值，一次方差，同时算法有两个可控参数：邻域半径r和方差调整变量delta，通过控制delta的大小，来控制图像的平滑程度与细节保留程度，当delta=0时，即为原图；
同时，专利指出该算法除了具有速度快，保边效果好的优点外，还避免了一般保边滤波器可能会出现的光晕现象，大家可以自己根据本人的DEMO进行测试，这里不再累赘。
这里在给出导向滤波的算法计算流程：
大家可以看一下，EPM滤波算法对比导向滤波，要少了不少的计算，主要包括三次均值计算和一次方差计算，因此耗时也要少很多；
本人用C代码实现的效果图如下：
通过效果对比，可以看到该算法具有一定的保边能力，可以做美颜磨皮算法。
该算法对比双边滤波效果图如下：
本人C代码如下：
//Edge Preserved mean filter
int EPMFilter(unsigned char* srcData, int width ,int height, int radius, float delta)
{
float *data = (float*)malloc(sizeof(float) * width * height);
float *meanIP = (float*)malloc(sizeof(float) * width * height);
float *corrIP = (float*)malloc(sizeof(float) * width * height);
for(int i = 0; i < width * height; i++)
{
data[i] = (float)srcData[i] / 255.0;
}
//mean and cov compute
MeanCovMapCalculate(data, width, height, meanIP, radius);
for(int i = 0; i < width * height; i++)
{
data[i] *= data[i];
}
//mean and cov compute
MeanCovMapCalculate(data, width, height, corrIP, radius);
for(int i = 0; i < width * height; i++)
{
corrIP[i] = corrIP[i] - meanIP[i] * meanIP[i];
}
for(int i = 0; i < width * height; i++)
{
float t = meanIP[i] + (corrIP[i] * (srcData[i] / 255.0f - meanIP[i]) / (corrIP[i] + delta));
srcData[i] = (unsigned char)(CLIP3(t * 255.0f, 0, 255));
}
free(data);
free(meanIP);
free(corrIP);
return 0;
};
//4通道处理
void f_EPMFilter(unsigned char* srcData, int nWidth, int nHeight, int nStride, int radius, float delta)
{
if (srcData == NULL)
{
return;
}
unsigned char* rData = (unsigned char*)malloc(sizeof(unsigned char) * nWidth * nHeight);
unsigned char* gData = (unsigned char*)malloc(sizeof(unsigned char) * nWidth * nHeight);
unsigned char* bData = (unsigned char*)malloc(sizeof(unsigned char) * nWidth * nHeight);
unsigned char* pSrc = srcData;
int Y, CB, CR;
unsigned char* pR = rData;
unsigned char* pG = gData;
unsigned char* pB = bData;
for(int j = 0; j < nHeight; j++)
{
for(int i = 0; i < nWidth; i++)
{
*pR = pSrc[2];
*pG = pSrc[1];
*pB = pSrc[0];
pR++;
pG++;
pB++;
pSrc += 4;
}
}
//并行处理
#pragma omp parallel sections num_threads(omp_get_num_procs())
{
#pragma omp section
EPMFilter(rData, nWidth, nHeight, radius, delta);
#pragma omp section
EPMFilter(gData, nWidth, nHeight, radius, delta);
#pragma omp section
EPMFilter(bData, nWidth, nHeight, radius, delta);
}
pSrc = srcData;
pR = rData;
pG = gData;
pB = bData;
int R, G, B;
for(int j = 0; j < nHeight; j++)
{
for(int i = 0; i < nWidth; i++)
{
pSrc[0] = *pB;
pSrc[1] = *pG;
pSrc[2] = *pR;
pR++;
pG++;
pB++;
pSrc += 4;
}
}
free(rData);
free(gData);
free(bData);
}
//单通道处理
void f_EPMFilterOneChannel(unsigned char* srcData, int nWidth, int nHeight, int nStride, int radius, float delta)
{
if (srcData == NULL)
{
return;
}
unsigned char* yData = (unsigned char*)malloc(sizeof(unsigned char) * nWidth * nHeight);
unsigned char* cbData = (unsigned char*)malloc(sizeof(unsigned char) * nWidth * nHeight);
unsigned char* crData = (unsigned char*)malloc(sizeof(unsigned char) * nWidth * nHeight);
unsigned char* pSrc = srcData;
int Y, CB, CR;
unsigned char* pY = yData;
unsigned char* pCb = cbData;
unsigned char* pCr = crData;
for(int j = 0; j < nHeight; j++)
{
for(int i = 0; i < nWidth; i++)
{
RGBToYCbCr(pSrc[2],pSrc[1],pSrc[0],&Y,&CB,&CR);
*pY = Y;
*pCb = CB;
*pCr = CR;
pY++;
pCb++;
pCr++;
pSrc += 4;
}
}
EPMFilter(yData, nWidth, nHeight, radius, delta);
pSrc = srcData;
pY = yData;
pCb = cbData;
pCr = crData;
int R, G, B;
for(int j = 0; j < nHeight; j++)
{
for(int i = 0; i < nWidth; i++)
{
YCbCrToRGB(*pY, *pCb, *pCr, &R, &G, &B);
pSrc[0] = B;
pSrc[1] = G;
pSrc[2] = R;
pY++;
pCb++;
pCr++;
pSrc += 4;
}
}free(yData);free(cbData);free(crData);
}EPM滤波算法由于具有保留边缘的作用，而且速度快，因此，在美颜磨皮算法中可以考虑。
最后给出一个测试DEMO：点击打开链接
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