圖像清晰度的評價算法

//eva 圖像清晰度評價算法

double EvaImageDefinition(IplImage *roiImg)
{
    _IplROI *rect=roiImg->roi;
	int height=rect->height;
	int width=rect->width;
	uchar *pData=(uchar *)roiImg->imageData;
	int step=roiImg->widthStep;
	int sumD1=0;
	int sumD2=0;
	for(int y=rect->yOffset+1;y<rect->yOffset+height-1;++y)
	{
		for(int x=rect->xOffset+1;x<rect->xOffset+rect->width-1;++x)
		{
			uchar value=pData[y*step+x];
			sumD1+=(abs(value-pData[(y-1)*step+x-1])+
                                        abs(value-pData[(y-1)*step+x+1])+
                                        abs(value-pData[(y+1)*step+x-1])+
                                        abs(value-pData[(y+1)*step+x+1]))*707; 

			sumD2+=abs(value-pData[y*step+x-1])+
                                        abs(value-pData[y*step+x+1])+
                                        abs(value-pData[(y+1)*step+x])+
                                        abs(value-pData[(y-1)*step+x]);
		}
	}
	return (sumD1/707.0+sumD2)/((height-2)*(width-2));
}

//Tenegrad 圖像清晰度評價算法 opencv實現

double TenegradImageDefinition (IplImage* src)
{
	IplImage* sobelX = cvCreateImage( cvSize(src->width,src->height), IPL_DEPTH_16S, 1 );
	IplImage* sobelY = cvCreateImage( cvSize(src->width,src->height), IPL_DEPTH_16S, 1 );
	IplImage* sum = cvCreateImage( cvSize(src->width,src->height), IPL_DEPTH_16S, 1 );
	cvSobel(src,sobelX,1,0,3);
	cvSobel(src,sobelY,0,1,3);
	cvAbs(sobelX,sobelX);
	cvAbs(sobelY,sobelY);
	cvAdd(sobelX,sobelY,sum);
	CvScalar scalar=cvSum(sum);

	cvReleaseImage(&sobelX);
	cvReleaseImage(&sobelY);
	cvReleaseImage(&sum);
	return scalar.val[0]/(src->width*src->height);

}