滤波源程序代码

例8-2 clear all; I=imread('circuit.tif'); figure; subplot(221);imshow(I); subplot(222);imhist(I); I1=histeq(I); subplot(223);imshow(I1); subplot(224);imhist(I1); 例8-4 I=imread('circuit.tif'); [M,N]=size(I); for i=1:8:257 counts(i)=i; end Q=imread('circuit.tif'); N=histeq(Q,counts); figure; subplot(221);imshow(N); subplot(222);imhist(N); axis([0 256 0 5000]); 例8-5 clear; a=imread('coins.png'); %读取图像 anoised=imnoise(a,'gaussian',0.1,0.005); %对图像进行高斯加噪 %制定卷积核 h=ones(3,3)/5; h(1,1)=0; h(1,3)=0; h(3,1)=0; h(1,3)=0; %平滑运算 a2=imfilter(anoised,h); subplot(131); imshow(a); %显示原始图像 subplot(132); imshow(anoised); %加有高斯噪声的图像 subplot(133); imshow(a2); %经过平滑后的图像 例8-6 clear all; I=imread('eight.tif'); J=imnoise(I,'salt & pepper',0.02); subplot(231);imshow(I); subplot(232);imshow(J); K1=filter2(fspecial('average',3),J); %进行3×3平滑滤波 K2=filter2(fspecial('average',5),J); %%进行5×5模板平滑滤波 K3=filter2(fspecial('average',7),J); %%进行7×7模板平滑滤波 K4=filter2(fspecial('average',9),J); %%进行9×9模板平滑滤波 subplot(233);imshow(uint8(K1)); subplot(234);imshow(uint8(K2)); subplot(235);imshow(uint8(K3)); subplot(236);imshow(uint8(K4)); 例8-7 a=imread('coins.png'); %读取图像 b=imnoise(a,'salt & pepper',0.02); %对图像增加椒盐噪声 k=medfilt2(b); %中值滤波去噪 subplot(131); imshow(a); xlabel('原始图像'); subplot(132); imshow(b);xlabel('含有椒盐噪声图像'); subplot(133); imshow(k);xlabel('中值滤波去噪图像'); 例8-9 I=imread('\lena.bmp'); J=im2double(I); subplot(2,2,1),imshow(J,[]) h1=[0 -1 0, -1 5 -1,0 -1 0]; h2=[-1 -1 -1, -1 9 -1,-1 -1 -1]; h3=[1 -2 0, -2 5 -2,1 -2 1]; A=conv2(J,h1,'same'); subplot(2,2,2), imshow(A,[]) B=conv2(J,h2,'same'); subplot(2,2,3),imshow(B,[]) C=conv2(J,h3,'same'); subplot(2,2,4),imshow(C,[]) 例8-10 clear all; [I,map]=imread('lena.bmp'); figure(1),imshow(I,map); I=double(I); [IX,IY]=gradient(I); GM=sqrt(IX.*IX+IY.*IY); meth1=GM; figure(2),imshow(meth 1,map); meth 2=I; J=find(GM>10); meth 2(J)=GM(J); figure(3),imshow(meth 2,map); meth 3=I; J=find(GM>10); meth 3(J)=255; figure(4),imshow(meth 3,map); meth 4=I; J=find(GM<10); meth 4(J)=255; figure(5),imshow(meth 4,map); meth 5=I; J=find(GM>10); meth 5(J)=255; Q=find(GM<10); OUTS(Q)=0; figure(6),imshow(meth 5,map); 例8-11 I=imread('cat.jpg'); h1=[0,-1,0;-1,5,-1;0,-1,0]; h2=[-1,-1,-1;-1,9,-1;-1,-1,-1]; BW1=imfilter(I,h1); BW2=imfilter(I,h2); figure; subplot(131);imshow(I); subplot(132);imshow(BW1); subplot(133);imshow(BW2); 例8-12 clear; I=imread('lena.bmp'); I1=double(I); h1=fspecial('sobel'); I2=filter2(h1,I); figure; subplot(121);imshow(I); I3=I1-I2; subplot(122);imshow(I3,[]); 例8-13 clear all; %实现巴特沃斯低通滤波器 I=imread('saturn.png'); J=imnoise(I,'salt & pepper',0.02); %给原图像加入椒盐噪声 figure; subplot(121);imshow(J); J=double(J); %采用傅里叶变换 f=fft2(J); %数据矩阵平衡 g=fftshift(f); [m,n]=size(f); N=3; d0=20; n1=floor(m/2); n2=floor(n/2); for i=1:m for j=1:n d=sqrt((i-n1)^2+(j-n2)^2); h=1/(1+(d/d0)^(2*N)); g(i,j)=h*g(i,j); end end g=ifftshift(g); g=uint8(real(ifft2(g))); subplot(122);imshow(g); 例8-14 clear all; % 频域高通滤波法对图像进行增强 [I,map]=imread('lena.bmp'); noisy=imnoise(I,'gaussian',0.01); %原图中加入高斯噪声 [M N]=size(I); F=fft2(noisy); fftshift(F); Dcut=100; D0=250; D1=150; for u=1:M for v=1:N D(u,v)=sqrt(u^2+v^2); %巴特沃斯高通滤波器传递函数 BUTTERH(u,v)=1/(1+(sqrt(2)-1))*(Dcut/D(u,v))^2; EXPOTH(u,v)=exp(log(1/sqrt(2))*(Dcut/D(u,v))^2); %指数高通滤波器传递函数 if D(u,v)=1 & yr(i)<=m) if f(yr(i),xr(i))==1 if xmin==0 xmin=i; end xmax=i; end end end if tetadetect(n1)~=0 x=xmin-1:xmax-1; y=y(x+1); else y=xmin-1:xmax-1; x=x(y+1); end y=m-1-y; plot(x,y,'linewidth',1); hold on; end axis([0,m-1,0,n-1]); title('Hough变换检测出的直线'); 例9-5 f=imread('woman.bmp'); subplot(121); imshow(f); title('原始图像'); f=double(f); T=(min(f(:))+max(f(:)))/2; done=false; i=0; while ~done r1=find(f<=T); r2=find(f>T); Tnew=(mean(f(r1))+mean(f(r2)))/2; done=abs(Tnew-T)<1; T=Tnew; i=i+1; end f(r1)=0; f(r2)=1; subplot(122); imshow(f); title('迭代阈值二值化图像'); 例9-6 f=imread('lena.bmp'); subplot(121); imshow(f); title('原始图像'); T=graythresh(f); g=im2bw(f,T); subplot(122); imshow(g); title('Otsu方法二值化图像'); 例9-7 clear; I=imread('eight.tif'); subplot(221); imshow(I); title('原始图像'); subplot(222); I=double(I); %计算距离函数 hv=fspecial('prewitt'); hh=hv.'; gv=abs(imfilter(I,hv,'replicate')); gh=abs(imfilter(I,hh,'replicate')); g=sqrt(gv.^2+gh.^2); % watershed算法分割 subplot(222); L=watershed(g); wr=L==0; imshow(wr); title('分水岭'); I(wr)=255; subplot(223); imshow(uint8(I)); title('分割结果'); %取出梯度图中局部极小值点 rm=imregionalmin(g); subplot(224); imshow(rm); title('局部极小值'); 例9-8 clear; I=imread('eight.tif'); subplot(231); imshow(I); %计算梯度图 I=double(I); hv=fspecial('prewitt'); hh=hv.'; gv=abs(imfilter(I,hv,'replicate')); gh=abs(imfilter(I,hh,'replicate')); g=sqrt(gv.^2+gh.^2); %计算距离函数 subplot(232); df=bwdist(I); imshow(uint8(df*8)); %计算外部约束 L=watershed(df); em=L==0; subplot(233); imshow(em); % 计算内部约束 im=imextendedmax(I,20); subplot(234); imshow(im); %重构梯度图 g2=imimposemin(g,im|em); subplot(235); imshow(g2); % watershed算法分割 L2=watershed(g2); wr2=L2==0; subplot(236); I(wr2)=255; imshow(uint8(I)); 例9-9 clear all; %假设下面的I是一幅灰度图像,取阈值0.5进行四叉树分解 I=[1 1 1 1 2 3 6 6; 1 1 10 1 4 5 6 8; 1 1 1 1 10 15 7 7; 1 1 1 1 10 25 7 7; 10 22 10 22 1 2 3 4; 10 22 22 10 5 6 7 8; 10 22 10 10 9 10 11 12; 22 22 10 10 13 14 15 16]; figure; image(I,'Cdatamapping','scaled'); axis ij;colormap gray S=qtdecomp(I,0.5); full(S) 例9-12 I=imread('image\duck.png'); subplot(221); imshow(I); title('原始图像'); b=imread('background.png'); subplot(222); imshow(b); title('背景图像'); df=im2double(I); db=im2double(b); c=df-db; d=im2uint8(c); subplot(223); imshow(d); title('差值图像'); T=50; T=T/255; i=find(abs(c)>=T); c(i)=1; i=find(abs(c)