椒盐噪声和中值滤波

王柯Salt-and-pepper noise1. 产生椒盐噪声在图像中随机选取6000个点，3000设为白色，3000个设为黑色，产生椒盐噪声%-%-%#include #include #include void salt(cv:Mat &image,int n)for(int k=0;kn;k+)int i=rand()%image.cols;int j=rand()%image.rows; if(image.channels()=1)image.at(j,i)=255;else if (image.channels() = 3) image.at(j,i)0= 255; image.at(j,i)1= 255; image.at(j,i)2= 255; /产生3000个白点for(int k=0;kn;k+)int i=rand()%image.cols;int j=rand()%image.rows; if(image.channels()=1)image.at(j,i)=0;else if (image.channels() = 3) image.at(j,i)0= 0; image.at(j,i)1= 0; image.at(j,i)2= 0; /产生3000个黑点int main()cv:Mat image;image = cv:imread(lena.jpg);salt(image,3000);cv:namedWindow(WK);cv:imshow(WK,image);cv:waitKey(0);return 1;%-%-%2. 中值滤波利用opencv中的cvSmooth对图像进行中值滤波。%-%-%#include #include #include int main()IplImage * image = cvLoadImage(wks.jpg);/cvNamedWindow(before);/cvShowImage(before,image);IplImage * im_gray = cvCreateImage(cvSize(image-width,image-height), IPL_DEPTH_8U, 1);cvCvtColor(image, im_gray, CV_BGR2GRAY);cvNamedWindow(Gray);cvShowImage(Gray,im_gray);cvSaveImage(im_gray.jpg,im_gray);IplImage * im_median_filter = cvCreateImage(cvSize(image-width,image-height), IPL_DEPTH_8U, 1);cvSmooth(im_gray, im_median_filter, CV_MEDIAN);/默认窗口大小为*3cvNamedWindow(after);cvShowImage(after,im_median_filter);/cvSaveImage(im_median.jpg,im_median_filter);cv:waitKey(0);return 1;%-%-%处理前的灰度图像： 处理后的灰度图像： 原始灰度图像：3.结论：可以看出中值滤波能够较好的恢复有椒盐噪声的图像4Matlab中值滤波：clear;clc;image = imread(gray.jpg);result= media_filter(image,7);imshow(result);imwrite(result,media_filter.jpg);%functionfunction d = media_filter(image,m)n=m;height,width=size(image);x1 = double(image);x2 = x1;for i=1:height-n+1 for j=1:width-n+1 A = x1(i:(i+n-1),j:(j+n-1); B = sort(A); C = sort(B); x2(i+(n-1)/2,j+(n-1)/2) = C(n+1)/2,(n+1)/2); endendd = uint8(x2);5.MATLAB中值滤波结果及结论：中值滤波是图像处理中的一个常用步骤，它对于斑点噪声和椒盐噪声来说尤其有用。中值滤波方法以牺牲图像清晰度为代价消除图像中的椒盐噪声。同时，中值滤波不能多次使用，因为这样会使图像各个像素间趋于相同，整个图像就会变得越来越模糊。7x7 11x11 2次11x11GaussNoise1.利用MATLAB向原始灰度图像中添加高斯噪声%clear;clc;image = imread(origin.jpg);noise = imnoise(image,gaussian);% 添加均值为0，方差为0.01的高斯白噪声imshow(noise);title(gaussian noise)%2.利用均值滤波clear;clc;image = imread(gaussnoise.jpg);result = avg_filter(image,11);imshow(result);imwrite(result,use11x11.jpg)function d=avg_filter(x,n)%x为待处理的图像，n为模板大小a(1:n,1:n)=1;height,width=size(x);x1=double(x);x2=x1;for i=1:height-n+1 for j=1:width-n+1 c = x1(i:(i+n-1),j:(j+n-1).*a;%取出x1中从(i,j)开始的n行n列元素与模板相乘 s=sum(sum(c); x2(i+(n-1)/2,j+(n-1)/2)=s/(n*n);%将与