椒鹽噪聲和中值濾波

1、Salt-and-peppernoise產(chǎn)生椒鹽噪聲在圖像中隨機(jī)選取6000個(gè)點(diǎn)，3000設(shè)為白色，3000個(gè)設(shè)為黑色，產(chǎn)生椒鹽噪聲%#include#include#includevoidsalt(cv:Mat&image,intn)for(intk=0;kn;k+)inti=rand()%image.cols;intj=rand()%image.rows;if(image.channels()=1)image.at(j,i)=255;elseif(image.channels()=3)image.at(j,i)0=255;image.at(j,i)1=255;image.at(j,i)2=

2、255;/產(chǎn)生3000個(gè)白點(diǎn)for(intk=0;kn;k+)inti=rand()%image.cols;intj=rand()%image.rows;if(image.channels()=1)image.at(j,i)=0;elseif(image.channels()=3)image.at(j,i)0=0;image.at(j,i)1=0;image.at(j,i)2=0;/產(chǎn)生3000個(gè)黑點(diǎn)intmain()cv:Matimage;image=cv:imread(lena.jpg);salt(image,3000);cv:namedWindow(WK);cv:imshow(WK,im

3、age);cv:waitKey(0);return1;%2.中值濾波利用opencv中的cvSmooth對(duì)圖像進(jìn)行中值濾波。%#include#include#includeintmain()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);

4、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);/默認(rèn)窗大小為*3cvNamedWindow(after);cvShowImage(after,im_median_filter);/cvSaveImage(im_media

5、n.jpg,im_median_filter);cv:waitKey(0);return1;%處理前的灰度圖像：處理后的灰度圖像：1tip處理前的灰度圖像：處理后的灰度圖像：1tip原始灰度圖像:3.結(jié)論:可以看出中值濾波能夠較好的恢復(fù)有椒鹽噪聲的圖像4.Matlab中值濾波:clear;clc;image=imread(gray.jpg);result=media_filter(image,7);imshow(result);imwrite(result,media_filter.jpg);%functionfunctiond=media_filter(image,m)n=m;height,

6、width=size(image);x1=double(image);x2=x1;fori=1:height-n+1forj=1:width-n+1A=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中值濾波結(jié)果及結(jié)論：中值濾波是圖像處理中的一個(gè)常用步驟，它對(duì)于斑點(diǎn)噪聲和椒鹽噪聲來說尤其有用。中值濾波方法以犧牲圖像清晰度為代價(jià)消除圖像中的椒鹽噪聲。同時(shí)，中值濾波不能多次使用因?yàn)檫@樣會(huì)使圖像各個(gè)像素間趨于相同，整個(gè)圖像就會(huì)變

7、得越來越模糊。7x711x11次11X11GaussNoise1.利用MATLAB向原始灰度圖像中添加高斯噪聲%clear;clc;image=imread(origin.jpg);noise=imnoise(image,gaussian);%添加均值為0，方差為0.01的高斯白噪聲imshow(noise);title(gaussiannoise)%利用均值濾波clear;clc;image=imread(gaussnoise.jpg);result=avg_filter(image,11);imshow(result);imwrite(result,use11x11.jpg)functiond=avg_filter(x,n)%x為待處理的圖像，n為模板大小a(1:n,1:n)=1;height,width=size(x);x1=double(x);x2=x1;fori=1:height-n+1forj=1:width-n+1c=x1(i:(i+n-1),j:(j+n-1).*a;取出x1中從(i,j)開始的n行n列元素與模板相乘s=sum(sum(c);x2(i+(n-1)/2,j+(n-1)/2)=s/(n*n);將與模板運(yùn)算后的各元素的均值賦給模板中心位置的元素endend%未被賦值的元素取原值d=uint8(x2);的團(tuán)像