VC++高斯滤波\中值滤波实现图像模糊处理

时间：2015-02-14 17:34:16 阅读：270 评论：0 收藏：0 [点我收藏+]

标签：

一、算法

高斯模糊算法详见：高斯模糊，基本思想就是利用高斯函数，将一个坐标点的所有邻域的加权平均值设置为这些点的颜色值。

中值滤波算法就更简单了：将一个坐标点的所有邻域的平均值设置为这些点的像素值。

二、算法的代码实现

高斯函数：技术分享

使用宏定义来替换：

#define PI<span style="white-space:pre">	</span>3.1415926
//高斯模糊函数
#define GAUSS_FUN(x, y) (exp(-(x*x)/(double)(2*y*y)))/(sqrt(2.0*PI)*y)
#define SQUARE(x)<span style="white-space:pre">	</span>((x)*(x))

//暂且定义sigma为10
const double sigma=10;

高斯模糊算法处理像素

//高斯模糊，nRadius为平均取值的半径，半径越大，图像越模糊，处理时间也越长
bool GaussFilter( DWORD* pData, DWORD* pCopy, const int nWidth, const int nHeight, const int nRadius/*=1*/ )
{
	if ( nWidth<=0 || nHeight<=0 || nRadius<=0 )
		return false;
	for ( int ny=0; ny<nHeight; ++ny )
	{
		for ( int nx=0; nx<nWidth; ++nx )
		{
			vector<COLOR_DATA> cdList;
			cdList.reserve(200);
			COLOR_DATA cd;
			double dTotal=0;
			for ( int m=nx-nRadius; m<=nx+nRadius; ++m )
			{
				if ( m<0 || m>=nWidth ) continue;
				for ( int n=ny-nRadius; n<=ny+nRadius; ++n )
				{
					if ( n<0 || n>=nHeight ) continue;
					cd.dDistance=GAUSS_FUN(sqrt((double)(SQUARE(m-nx)+SQUARE(n-ny))), sigma);
					dTotal+=cd.dDistance;
					cd.dwColor=*(pData+n*nWidth+m);
					cdList.push_back(cd);
				}
			}
			if ( cdList.size()>0 )
			{//这里来计算整个邻域内所有像素点的加权平均值

				std::vector<COLOR_DATA>::const_iterator itor=cdList.begin();
				double r=0, g=0, b=0;
				for ( ; itor!=cdList.end(); ++itor ) 
				{
					double dRate=itor->dDistance/dTotal;//距离中心点越远，权值越小
					r+=GetRValue(itor->dwColor)*dRate;
					g+=GetGValue(itor->dwColor)*dRate;
					b+=GetBValue(itor->dwColor)*dRate;
				}
				*(pCopy+ny*nWidth+nx)=RGB((int)r, (int)g, (int)b);
			}
		}
	}
	return true;
}

中值滤波函数就很简单了，不细说

//中值滤波
bool MedianFilter( DWORD* pData, DWORD* pCopy, const int nWidth, const int nHeight, const int nRadius )
{
	if ( nWidth<=0 || nHeight<=0 || nRadius<=0 )
		return false;
	for ( int ny=0; ny<nHeight; ++ny )
	{
		for ( int nx=0; nx<nWidth; ++nx )
		{//扫描每一个点的邻域，把他们的像素值保存起来。
			vector<DWORD> data;
			for ( int m=nx-nRadius; m<=nx+nRadius; ++m )
			{
				if ( m<0 || m>=nWidth || (m==nx) ) continue;
				for ( int n=ny-nRadius; n<=ny+nRadius; ++n )
				{
					if ( n<0 || n>=nHeight || (n==ny) ) continue;
					data.push_back(*(pData+n*nWidth+m));
				}
			}
			if ( data.size()>0 )
			{
				std::sort(data.begin(), data.end());//排序
				*(pCopy+ny*nWidth+nx)=data[data.size()/2];//取所有像素值的中值作为整个区域的像素值
			}
		}
	}
	return true;
}

线程函数中处理图像的像素，完成后发消息通知界面更新

DWORD __stdcall GaussThread(LPVOID lpParam)
{
	HLOCAL hMem=LocalAlloc(LHND, g_lBmpSize);
	DWORD* pBuffer=(DWORD*)LocalLock(hMem);
	LONG lCopySize=::GetBitmapBits(g_hBitmap1, g_lBmpSize, pBuffer);
	HLOCAL hMemCopy=LocalAlloc(LHND, g_lBmpSize);
	DWORD* pBufferCopy=(DWORD*)LocalLock(hMemCopy);
	//MedianFilter(pBuffer, bmMetric.bmWidth, bmMetric.bmHeight, 1);
	//MedianFilter(pBuffer, pBufferCopy, g_nBmpWidth, g_nBmpHeight, 3);
	//MedianFilterRGB(pBuffer, pBufferCopy, g_nBmpWidth, g_nBmpHeight, 10);
	GaussFilter(pBuffer, pBufferCopy, g_nBmpWidth, g_nBmpHeight, 6);
	::SetBitmapBits(g_hBitmap1, g_lBmpSize, pBufferCopy);
	LocalUnlock(hMem);
	LocalFree(hMem);
	LocalUnlock(hMemCopy);
	LocalFree(hMemCopy);
	::PostMessage(g_hMainWnd, WM_GAUSS_MSG, 0, 0);
	return 0;
}