几种常用的图像平滑方法算法

几种常用的图像平滑方法算法1、

二维自适应维纳滤波：

I=imread('C:\Documents and Settings\Administrator\桌面\1.gif'); %读取图像

J=imnoise(I,'gaussian',0,0.005); %加入均值为0，方差为0.005的高斯噪声

K2=wiener2(J,[3 3]); %对加噪图像进行二维自适应维纳滤波

K2=wiener2(J,[5 5]); %对加噪图像进行二维自适应维纳滤波

K2=wiener2(J,[7 7]); %对加噪图像进行二维自适应维纳滤波

K2=wiener2(J,[9 9]); %对加噪图像进行二维自适应维纳滤波

subplot(2,3,1);imshow(I);

title('原始图像');

subplot(2,3,2);imshow(J);

title('加噪图像');

subplot(2,3,3);imshow(K1);

title('恢复图像1');

subplot(2,3,4);imshow(K2);

title('恢复图像2');

subplot(2,3,5);imshow(K3);

title('恢复图像3');

subplot(2,3,6);imshow(K4);

title('恢复图像3');

2、

均值滤波、中值滤波、维纳滤波消除椒盐噪声

I=imread(1.gif');

J1=imnoise(I,'gaussian',0,0.02);

J2=imnoise(I,'salt & pepper',0.02);

J3=imnoise(I,'speckle',0.02);

运行效果见图2

I=imread('C:\Documents and Settings\Administrator\桌面\1.gif');

J=imnoise(I,'salt & pepper',0.02);

%h=ones(3,3)/9;%产生3*3的全1数组

%B=conv2(J,h);%卷积运算

K2=filter2(fspecial('average',3),J)/255; %均值滤波模板尺寸为3

K= medfilt2(J);%采用二维中值滤波函数medfilt2对受椒盐噪声干扰的图像滤波K1=wiener2(J,[3 3]); %对加噪图像进行二维自适应维纳滤波

subplot(2,3,1);imshow(I);

title('原始图像');

subplot(2,3,2);imshow(J);

title('加噪图像');

subplot(2,3,3);imshow(K2);

title('均值滤波后的图像');

subplot(2,3,4);imshow(K);

title('中值滤波后的图像');

subplot(2,3,5);imshow(K1);

title('维纳滤波后的图像');

3、

均值滤波对高斯噪声的效果

I=imread('C:\Documents and Settings\Administrator\桌面\1.gif');%读取图像J=imnoise(I,'gaussian',0,0.005);%加入均值为0，方差为0.005的高斯噪声subplot(2,3,1);imshow(I);

title('原始图像');

subplot(2,3,2); imshow(J);

title('加入高斯噪声之后的图像');

%采用MA TLAB中的函数filter2对受噪声干扰的图像进行均值滤波

K1=filter2(fspecial('average',3),J)/255; %模板尺寸为3

K2=filter2(fspecial('average',5),J)/255;% 模板尺寸为5

K3=filter2(fspecial('average',7),J)/255; %模板尺寸为7

K4= filter2(fspecial('average',9),J)/255; %模板尺寸为9

subplot(2,3,3);imshow(K1);

title('改进后的图像1');

subplot(2,3,4); imshow(K2);

title('改进后的图像2');

subplot(2,3,5);imshow(K3);

title('改进后的图像3');

subplot(2,3,6);imshow(K4);

title('改进后的图像4');

4、

利用wpdencmp函数进行图像去噪

I=imread('C:\Documents and Settings\Administrator\桌面\1.gif');

I=im2double(I);

subplot(2,2,1);imshow(I);

title('原始图像');

J=imnoise(I,'gaussian',0,0.05);

subplot(2,2,2);imshow(J);

title('含噪图像');

thr=0.1;sorh='s';

crit='shannon';

keepapp=0;

J1=wpdencmp(J,sorh,3,'sym4',crit,thr,keepapp);

subplot(2,2,3);imshow(J1);

title('全局阈值去噪图像');

J2=medfilt2(J1);

subplot(2,2,4);imshow(J2);

title('第二次去噪图像');

5、

利用wrcoef2函数进行图像去噪

I=imread('C:\Documents and Settings\Administrator\桌面\1.gif'); J=imnoise(I,'gaussian',0,0.005);

[c,l]=wavedec2(J,2,'sym4');

J1= wrcoef2('a',c,l,'sym4',1);

J2= wrcoef2('a',c,l,'sym4',2);

subplot(2,2,1);imshow(I);

title('原始图像');

subplot(2,2,2);imshow(J);

title('含噪图像');

subplot(2,2,3);imshow(J1,[]);

title('第一次去噪图像');

subplot(2,2,4);imshow(J2,[]);

title('第二次去噪图像');

6、

使用二维统计滤波对椒盐噪声和高斯噪声进行滤波

I=imread('C:\Documents and Settings\Administrator\桌面\1.gif'); J1=imnoise(I,'salt & pepper',0.004);

subplot(2,3,1);imshow(I);

title('原始图像');

subplot(2,3,2);imshow(J1);

title('加椒盐噪声后的图像');

J= ordfilt2(J1,5,ones(3,4));% 进行二维统计顺序过滤

subplot(2,3,3);imshow(J);

title('椒盐噪声滤波后的图像');

J2=imnoise(I,'gaussian',0,0.004);

subplot(2,3,4);imshow(J2);

title('加高斯噪声后的图像');

J3= ordfilt2(J2,5,ones(3,4));

subplot(2,3,5);imshow(J3);

title('高斯噪声滤波后的图像');