《学习OpenCV》第五章课后题1

题目说明：
载入一个带有有趣纹理的图像。使用cvSmooth（）函数以多种方法平滑图像，参数为smoothtype=CV_GAUSSIAN。
a.使用对称的平滑窗口，大小依次为3*3,5*5,9*9和11*11，并显示出结果。
b.用5*5高斯滤波器平滑图像两次和用11*11平滑器平滑一次的输出结果是接近相同吗？为什么？

#include <opencv/highgui.h>#include <opencv/cv.h>#include <math.h>/* *《学习OpenCV》第五章第一题   * 完成时间：18:37 10/13 星期日 2013   * 作者：[e-mail] *//* * function: calculate MSE & PSNR of two GrayScale(8-bit depth & one channel) images. * param: img1 -- the first image. * param: img2 -- the second image. * param: dMSE -- the MSE of two images(output) * param: dPSNR -- the PSNR of two images(output) * return: 0 -- success;  others -- failed. */int calculateGrayImgsPSNR(IplImage* img1, IplImage* img2, double& dMSE, double& dPSNR){    if( !img1 || !img2 ||         img1->nChannels != 1 ||        img2->nChannels != 1 ||         img1->depth != img2->depth ||        img1->width != img2->width ||         img1->height != img2->height )    {        return -1;    }    int width = img1->width;    int height = img1->height;    // calculate MSE of the two images    double dSumOfSquares = 0;    for(int i = 0; i < height; i++)    {        char* pdata1 = img1->imageData + i * img1->widthStep;        char* pdata2 = img2->imageData + i *img2->widthStep;        for(int j = 0; j < width; j++ )        {            uchar value1 = *(pdata1 + j);            uchar value2 = *(pdata2 + j);            double square = pow( (double)(value1 - value2), 2 );            dSumOfSquares += square;        }    }    dMSE = dSumOfSquares / (width * height);    // this is means the two images are strictly same.     if(dMSE == 0)    {        dPSNR = -1;        return 0;    }    int iDepth = img1->depth;    int iMAX = pow( 2., iDepth) - 1;    dPSNR = 20 * log10(iMAX / (sqrt(dMSE)));    return 0;}int main(){    const char * FILE_PATH = "du.jpg";    IplImage* src = cvLoadImage(FILE_PATH, CV_LOAD_IMAGE_UNCHANGED);    if(!src)    {        printf("Load image error.\n");        return -1;    }    // Get the source image's size    CvSize srcSize = cvGetSize(src);    // 3 * 3    IplImage* dst_three_gaussian = cvCreateImage(srcSize, src->depth, src->nChannels);    // 5 * 5    IplImage* dst_five_gaussian = cvCreateImage(srcSize, src->depth, src->nChannels);    // 9 * 9    IplImage* dst_nine_gaussian = cvCreateImage(srcSize, src->depth, src->nChannels);    // 11 * 11    IplImage* dst_eleven_gaussian = cvCreateImage(srcSize, src->depth, src->nChannels);    // twice 5 * 5    IplImage* dst_twice_five_gaussian = cvCreateImage( srcSize, src->depth, src->nChannels );    if( !dst_three_gaussian || !dst_five_gaussian ||        !dst_nine_gaussian || !dst_eleven_gaussian ||        !dst_twice_five_gaussian )    {        printf("Create image error.\n");        return -1;    }    cvSmooth(src, dst_three_gaussian, CV_GAUSSIAN, 3, 3);    cvSmooth(src, dst_five_gaussian, CV_GAUSSIAN, 5, 5);    cvSmooth(src, dst_nine_gaussian, CV_GAUSSIAN, 9, 9);    cvSmooth(src, dst_eleven_gaussian, CV_GAUSSIAN, 11, 11);    cvSmooth( dst_five_gaussian, dst_twice_five_gaussian, CV_GAUSSIAN, 5, 5 );    cvShowImage("src", src);    cvShowImage("src - GAUSSIAN 3*3", dst_three_gaussian);    cvShowImage("src - GAUSSIAN 5*5", dst_five_gaussian);    cvShowImage("src - GAUSSIAN 9*9", dst_nine_gaussian);    cvShowImage("src - GAUSSIAN 11*11", dst_eleven_gaussian);    cvShowImage("src - GAUSSIAN 5*5 Twice", dst_twice_five_gaussian );    // calculate the MSE and PSNR of the two images.    double dMSE, dPSNR;    // part a:    calculateGrayImgsPSNR(src, dst_three_gaussian, dMSE, dPSNR);    printf("source image & 3*3 GAUSSIAN: MSE: %f\tPSNR: %f\n", dMSE, dPSNR);    calculateGrayImgsPSNR(src, dst_five_gaussian, dMSE, dPSNR);    printf("source image & 5*5 GAUSSIAN: MSE: %f\tPSNR: %f\n", dMSE, dPSNR);    calculateGrayImgsPSNR(src, dst_nine_gaussian, dMSE, dPSNR);    printf("source image & 9*9 GAUSSIAN: MSE: %f\tPSNR: %f\n", dMSE, dPSNR);    calculateGrayImgsPSNR(src, dst_eleven_gaussian, dMSE, dPSNR);    printf("source image & 11*11 GAUSSIAN: MSE: %f\tPSNR: %f\n", dMSE, dPSNR);    // part b    puts("---------------------------\n");    calculateGrayImgsPSNR(src, dst_eleven_gaussian, dMSE, dPSNR);    printf("source image & eleven: MSE: %f\tPSNR: %f\n", dMSE, dPSNR);    calculateGrayImgsPSNR(src, dst_twice_five_gaussian, dMSE, dPSNR);    printf("source image & twice five: MSE: %f\tPSNR: %f\n", dMSE, dPSNR);    calculateGrayImgsPSNR(dst_eleven_gaussian, dst_twice_five_gaussian, dMSE, dPSNR);    printf("eleven & twice five: MSE: %f\tPSNR: %f\n", dMSE, dPSNR);    cvWaitKey(0);    cvReleaseImage(&src);    cvReleaseImage(&dst_three_gaussian);    cvReleaseImage(&dst_five_gaussian);    cvReleaseImage(&dst_nine_gaussian);    cvReleaseImage(&dst_eleven_gaussian);    cvReleaseImage(&dst_twice_five_gaussian);    cvDestroyAllWindows();    return 0;}

参考：
峰值信噪比PSNR：
百度百科
http://baike.baidu.com/link?url=x0Q57KBhTHetM4s32tbqiD_2VmrCUIgonLMjFvx3RkCeaPxYYcjeVNI24X792rP2_VKns7QULDyh7BFESqCuwa
b问题：因为5*5两次高斯滤波可以说是对每一个点的10*10范围滤波处理，我们知道高斯滤波的高斯滤波还是高斯滤波，所以5*5两次高斯滤波（10*10范围滤波处理）与11*11的滤波结果是相似的。

引用qdsclove的专栏
http://blog.csdn.net/stk_overflow/article/details/12683117