实验内容及实验原理：
1．用均值滤波器（即邻域平均法）去除图像中的噪声；
2．用中值滤波器去除图像中的噪声
3．比较两种方法的处理结果
实验步骤：
用原始图像lena.bmp或cameraman.bmp加产生的3%椒盐噪声图像合成一幅有噪声的图像并显示；
1．用均值滤波器去除图像中的噪声（选3x3窗口）：以当前像素点为中心，求窗口内所有灰度值的和，以其平均值作为中心像素新的灰度值
2． 用中值滤波器去除图像中的噪声(选3x3窗口做中值滤波)；以当前像素点为中心，求窗口中所有像素点的灰度值的中值，以中值作为中心像素点的值
3．将两种处理方法的结果与原图比较，注意两种处理方法对边缘的影响。
添加椒盐噪声
以3%的几率产生随机为0/255的灰度值覆盖原来的灰度值
for (int i = 0; i <src->height; i++){for (int j = 0; j <src->width; j++){CvScalar s = cvGet2D(src, i,j);srand(seed++);if (rand() % 100 <3){//3%的几率覆盖srand(seed++);s.val[0]= rand() % 2 * 255;//随机是0还是255}cvSet2D(dst,i, j, s);}}添加后：
均值滤波
计算当前像素周围的九个像素点的灰度值和，以其平均值作为当前像素的灰度值。如果是边缘的像素点就求有限的几个像素灰度值的平均值
for (int i = 0;i < dst->height; i++){for (int j = 0; j < dst->width; j++){CvScalar s =cvGet2D(dst, i, j);double sum =0;//存放窗口中所有像素点灰度值的和int num =0;//记录窗口中像素的个数，因为边界上可能不是9个像素//求3x3的和for (int k = -1; k <= 1; k++)for (int m = -1; m <= 1; m++)if (i + k>= 0 && i + k <= 255 && j + m >= 0 && j + m<= 255){CvScalar temp =cvGet2D(dst, i+k, j+m);sum += temp.val[0];num++;}s.val[0] = sum/num;cvSet2D(dst_sp, i, j, s);}
}
中值滤波
获取当前像素位置周围的九个像素灰度值，排序选择中间值作为新的灰度值。如果是边界的像素点就只选择有限的几个灰度值的中值
for (int i = 0;i < dst->height; i++){for (int j = 0; j < dst->width; j++){CvScalar s =cvGet2D(dst, i, j);double val[9]= {0},mid=0;//分别为存放窗口中所有灰度值的数组和中值int num =0;//记录实际窗口中有几个像素点//遍历当前像素点为中心的3X3窗口for (int k = -1; k <= 1; k++)for (int m = -1; m <= 1; m++)if (i + k>= 0 && i + k <= 255 && j + m >= 0 && j + m<= 255){//在图像上，没有超出边界CvScalar temp =cvGet2D(dst, i + k, j + m);val[num++]= temp.val[0];}//冒泡排序，从大到小for (int k = 0; k < 9; k++){for (int m = 0; m < 8-k; m++){if (val[m]< val[m + 1]){double temp =val[m];val[m] = val[m + 1];val[m + 1] = temp;}}}s.val[0] = val[num/2];//求中值cvSet2D(dst_sp2, i, j, s);}
}
源码：
// opencv1.cpp: 定义控制台应用程序的入口点。
//#include "stdafx.h"
#include<iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <cv.h>
#include <cxcore.h>
#include <cvaux.h>
#include <stdlib.h>
#include <imgproc.hpp>
#include <time.h>using namespace cv;int main()
{IplImage *src;src = cvLoadImage("lena.bmp", 1);//原图IplImage *dst = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, 1);std::cout << "添加椒盐噪声" << std::endl;int seed = 1;for (int i = 0; i < src->height; i++){for (int j = 0; j < src->width; j++){CvScalar s = cvGet2D(src, i, j);srand(seed++);if (rand() % 100 < 3){srand(seed++);s.val[0] = rand() % 2 * 255;}cvSet2D(dst, i, j, s);}}cvNamedWindow("Image1", 1);//创建窗口cvShowImage("Image1", dst);//显示图像IplImage *dst_sp = cvCreateImage(cvSize(dst->width, dst->height), IPL_DEPTH_8U, 1);std::cout << "均值滤波" << std::endl;for (int i = 0; i < dst->height; i++){for (int j = 0; j < dst->width; j++){CvScalar s = cvGet2D(dst, i, j);double sum = 0;int num = 0;//求3x3的和for (int k = -1; k <= 1; k++)for (int m = -1; m <= 1; m++)if (i + k >= 0 && i + k <= 255 && j + m >= 0 && j + m <= 255){CvScalar temp = cvGet2D(dst, i+k, j+m);sum += temp.val[0];num++;}s.val[0] = sum/num;cvSet2D(dst_sp, i, j, s);}}cvNamedWindow("Image2", 1);//创建窗口cvShowImage("Image2", dst_sp);//显示图像IplImage *dst_sp2 = cvCreateImage(cvSize(dst->width, dst->height), IPL_DEPTH_8U, 1);std::cout << "中值滤波" << std::endl;for (int i = 0; i < dst->height; i++){for (int j = 0; j < dst->width; j++){CvScalar s = cvGet2D(dst, i, j);double val[9] = {0},mid=0;int num = 0;for (int k = -1; k <= 1; k++)for (int m = -1; m <= 1; m++)if (i + k >= 0 && i + k <= 255 && j + m >= 0 && j + m <= 255){CvScalar temp = cvGet2D(dst, i + k, j + m);val[num++]= temp.val[0];}//排序，从大到小for (int k = 0; k < 9; k++){for (int m = 0; m < 8-k; m++){if (val[m] < val[m + 1]){double temp = val[m];val[m] = val[m + 1];val[m + 1] = temp;}}}s.val[0] = val[num/2];cvSet2D(dst_sp2, i, j, s);}}cvNamedWindow("Image3", 1);//创建窗口cvShowImage("Image3", dst_sp2);//显示图像cvWaitKey(0); //等待按键cvReleaseImage(&dst_sp2); //释放图像cvReleaseImage(&dst_sp); //释放图像cvReleaseImage(&dst); //释放图像
}原图：（原来是bmp格式的，但是不能上传，改成png格式了，分辨率都是256X256的）
