11. 对一幅灰度图像增加高频噪声，再对其进行频域低通滤波。

#include <cv.h>
#include <highgui.h> //图像视频输出/输入头文件
using namespace std;

IplImage* AddGuassianNoise(IplImage* src)    //添加高斯噪声
{
    IplImage* dst = cvCreateImage(cvGetSize(src), src->depth, src->nChannels);
    IplImage* noise = cvCreateImage(cvGetSize(src), src->depth, src->nChannels);
    CvRNG rng = cvRNG(-1);
    cvRandArr(&rng, noise, CV_RAND_NORMAL, cvScalarAll(0), cvScalarAll(15));
    cvAdd(src, noise, dst);
    return dst;
}
IplImage* AddPepperNoise(IplImage* src)      //添加胡椒噪声，随机黑色点
{
    IplImage* dst = cvCreateImage(cvGetSize(src), src->depth, src->nChannels);
    cvCopy(src, dst);
    for (int k = 0; k<8000; k++)
    {
        int i = rand() % src->height;
        int j = rand() % src->width;
        CvScalar s = cvGet2D(src, i, j);
        if (src->nChannels == 1)
        {
            s.val[0] = 0;
        }
        else if (src->nChannels == 3)
        {
            s.val[0] = 0;
            s.val[1] = 0;
            s.val[2] = 0;
        }
        cvSet2D(dst, i, j, s);
    }
    return dst;
}
IplImage* AddSaltNoise(IplImage* src)       //添加盐噪声，随机白色点
{
    IplImage* dst = cvCreateImage(cvGetSize(src), src->depth, src->nChannels);
    cvCopy(src, dst);
    for (int k = 0; k<8000; k++)
    {
        int i = rand() % src->height;
        int j = rand() % src->width;
        CvScalar s = cvGet2D(src, i, j);
        if (src->nChannels == 1)
        {
            s.val[0] = 255;
        }
        else if (src->nChannels == 3)
        {
            s.val[0] = 255;
            s.val[1] = 255;
            s.val[2] = 255;
        }
        cvSet2D(dst, i, j, s);
    }
    return dst;
}
IplImage* AddPepperSaltNoise(IplImage* src)    //添加椒盐噪声，随机黑白点
{
    IplImage* dst = cvCreateImage(cvGetSize(src), src->depth, src->nChannels);
    cvCopy(src, dst);
    for (int k = 0; k<8000; k++)
    {
        int i = rand() % src->height;
        int j = rand() % src->width;
        int m = rand() % 2;
        CvScalar s = cvGet2D(src, i, j);
        if (src->nChannels == 1)
        {
            if (m == 0)
            {
                s.val[0] = 255;
            }
            else
            {
                s.val[0] = 0;
            }
        }
        else if (src->nChannels == 3)
        {
            if (m == 0)
            {
                s.val[0] = 255;
                s.val[1] = 255;
                s.val[2] = 255;
            }
            else
            {
                s.val[0] = 0;
                s.val[1] = 0;
                s.val[2] = 0;
            }
        }
        cvSet2D(dst, i, j, s);
    }
    return dst;
}

//算术均值滤波器——模板大小5*5
IplImage* ArithmeticMeanFilter(IplImage* src)
{
    IplImage* dst = cvCreateImage(cvGetSize(src), src->depth, src->nChannels);
    cvSmooth(src, dst, CV_BLUR, 5);
    return dst;
}


int main()
{
    IplImage * test, *test_1, *test_2;
    test = cvLoadImage("6013202130.jpg",0);//图片路径是 ConsoleApplication4 文件夹里，同时实验要求转为灰度图片
    test_1 = cvCreateImage(cvSize((test->width), (test->height)), IPL_DEPTH_8U, 1); //创建图像，给指针赋值
    test_2 = cvCreateImage(cvSize((test->width), (test->height)), IPL_DEPTH_8U, 1); //创建图像，给指针赋值
    test_2 = AddGuassianNoise(test);
    
    test_1 = ArithmeticMeanFilter(test_2);


    cvNamedWindow("原图", CV_WINDOW_AUTOSIZE);
    cvShowImage("原图", test);
    cvNamedWindow("滤波后", CV_WINDOW_AUTOSIZE);
    cvShowImage("滤波后", test_1);
    cvNamedWindow("噪声后", CV_WINDOW_AUTOSIZE);
    cvShowImage("噪声后", test_2);
    cvWaitKey(0);//等待按键
    cvDestroyWindow("zhang_fei_1");
    cvDestroyWindow("zhang_fei_2");
    cvDestroyWindow("zhang_fei_3");
    cvReleaseImage(&test);//释放内存。 
    cvReleaseImage(&test_1);
    cvReleaseImage(&test_2);
    return 0;
}