数字图像处理(matlab)

图像反转：

代码：I=imread('coins.png');R=255-I;subplot(1,2,1);imshow(I);title('原图');subplot(1,2,2);imshow(R);title('反转后');
 
二值图像：

代码：I=imread('coins.png')YZmax=max(max(I))YZmin=min(min(I))initialYZ=(YZmax+YZmin)/2flag=1[m,n]=size(I)while(flag)   maxg=0   ming=0   maxgsum=0   mingsum=0    for i=1:m       for j=1:n           tmp=I(i,j)           if(tmp>initialYZ)                maxg=maxg+1                maxgsum=maxgsum+double(tmp)           else                ming=ming+1                mingsum=mingsum+double(tmp)           end       end    end   l1=maxgsum/maxg   l2=mingsum/ming   YZtmp=uint8((l1+l2)/2)    if(YZtmp==initialYZ)       flag=0;    else       initialYZ=YZtmp    endendnewI=im2bw(I,double(initialYZ)/255)imshow(newI)
 
 
 
 
 
 
 
 
 
 
 
 
8连通域：
 

代码：I=imread('coins.png');I=I>=128;%二值图像[m n]=size(I);I=[zeros(m,1) I  zeros(m,1)];I=[zeros(1,n+2);I;zeros(1,n+2)];top=1;tail=1;num=0;%连通域个数nb=[-1 -1;-1 0;-1 1;0 1;1 1;1 0;1 -1;0 -1];%8个方向for i=2:m+1    for j=2:n+1       if(I(i,j)==1)          num=num+1;          I(i,j)= mod((i+j),254)+1;%只是想每一块连通域显示不同的颜色          k=1;          p(top,1)=i;          p(top,2)=j;          p(top,3)=k;          top=top+1;          while top~=tail %站内没有东西就代表一个连通域查找结束              top=top-1;              x=p(top,1);              y=p(top,2);              d=p(top,3);              while d<=8%循环八个方向                  subx= x+nb(d,1);                  suby= y+nb(d,2);                                    if(I(subx,suby)==1)%如果这个点8个方向中有一个方向是连通的就把这个点压进栈                      p(top,1)=x;                      p(top,2)=y;                      p(top,3)=d+1;                      top=top+1;                      I(subx,suby)=mod((i+j),254)+1;                      x=subx;%然后利用新找到的连通点循环这个过程                      y=suby;                      d=0;                  end                                    d=d+1;              end          end       end    endenddisp(num);imshow(I,[]); 
图像加噪声：

代码：I=imread('cameraman.tif');J1=imnoise(I,'salt & pepper',0.1); %椒盐J2=imnoise(I,'gaussian',0.10); %gausssubplot(1,3,1)'imshow(I);title('原图');subplot(1,3,2)'imshow(J1);title('椒盐噪声');subplot(1,3,3)'imshow(J2);title('高斯噪声'); 
 
均值平滑：

代码：I=imread('cameraman.tif');J1=imnoise(I,'salt & pepper',0.1); %椒盐[m,n]=size(J1);J1=double(J1);subJ1=zeros(m,n);subJ1(1,1)=(J1(1,1)+J1(1,2)+J1(2,2)+J1(2,1))/4;%角的特殊情况subJ1(1,n)=(J1(1,n)+J1(1,n-1)+J1(2,n-1)+J1(2,n))/4;subJ1(m,1)=(J1(m,1)+J1(m,2)+J1(m-1,2)+J1(m-1,1))/4;subJ1(m,n)=(J1(m,n)+J1(m,n-1)+J1(m-1,n-1)+J1(m-1,n))/4;for i=2:n-1 %4条边的特殊情况   subJ1(1,i)=(J1(1,i-1)+J1(1,i)+J1(1,i+1)+J1(2,i-1)+J1(2,i)+J1(2,i+1))/6;endfor i=2:n-1 %4条边的特殊情况   subJ1(m,i)=(J1(m,i-1)+J1(m,i)+J1(m,i+1)+J1(m-1,i-1)+J1(m-1,i)+J1(m-1,i+1))/6;endfor i=2:m-1 %4条边的特殊情况   subJ1(i,1)=(J1(i-1,1)+J1(i,1)+J1(i+1,1)+J1(i-1,2)+J1(i,2)+J1(i+1,2))/6;endfor i=2:m-1 %4条边的特殊情况   subJ1(i,n)=(J1(i-1,n)+J1(i,n)+J1(i+1,n)+J1(i-1,n-1)+J1(i,n-1)+J1(i+1,n-1))/6;endfor i=2:m-1 %除了4个角和4条边的其他所有点的情况   for j=2:n-1      subJ1(i,j)=(J1(i-1,j-1)+J1(i-1,j)+J1(i-1,j+1)+J1(i,j-1)+J1(i,j)+J1(i,j+1)+J1(i+1,j-1)+J1(i+1,j)+J1(i+1,j+1))/9;   endendimshow(subJ1,[]);title('均值平滑'); 
中值平滑：

代码：I=imread('cameraman.tif');J1=imnoise(I,'gaussian',0.10); %gauss[m,n]=size(J1);subJ1=zeros(m,n);A=[J1(1,1) J1(1,2) J1(2,2) J1(2,1)];%角的特殊情况subJ1(1,1)=median(A);A=[J1(1,n) J1(1,n-1) J1(2,n-1) J1(2,n)];subJ1(1,n)=median(A);A=[J1(m,1) J1(m,2) J1(m-1,2) J1(m-1,1)];subJ1(m,1)=median(A);A=[J1(m,n) J1(m,n-1) J1(m-1,n-1) J1(m-1,n)];subJ1(m,n)=median(A);for i=2:n-1%边的特殊情况   A=[J1(1,i-1) J1(1,i) J1(1,i+1) J1(2,i-1) J1(2,i) J1(2,i+1)];   subJ1(1,i)=median(A);endfor i=2:n-1%边的特殊情况   A=[J1(m,i-1) J1(m,i) J1(m,i+1) J1(m-1,i-1) J1(m-1,i) J1(m-1,i+1)];   subJ1(m,i)=median(A);endfor i=2:m-1%边的特殊情况   A=[J1(i-1,1) J1(i,1) J1(i+1,1) J1(i-1,2) J1(i,2) J1(i+1,2)];   subJ1(i,1)=median(A);endfor i=2:m-1%边的特殊情况   A=[J1(i-1,n) J1(i,n) J1(i+1,n) J1(i-1,n-1) J1(i,n-1) J1(i+1,n-1)];   subJ1(i,n)=median(A);endfor i=2:m-1 %除了角和边的情况   for j=2:n-1      A=[J1(i-1,j-1) J1(i-1,j) J1(i-1,j+1) J1(i,j-1) J1(i,j) J1(i,j+1)J1(i+1,j-1) J1(i+1,j) J1(i+1,j+1)];      subJ1(i,j)=median(A);   endendimshow(subJ1,[]);
 
 
 
 
 
 
 
 
soble算子和laplace算子锐化

 
代码：I=imread('cameraman.tif');subplot(2,2,1),imshow(I);title('原始图像'); H=fspecial('sobel');     %选择sobel算子J=filter2(H,I1);            %卷积运算subplot(2,2,3),imshow(J); title('sobel算子锐化图像'); h=[0 1 0;1 -4 1;0 1 0];   %拉普拉斯算子J1=conv2(I,h,'same');           %卷积运算subplot(2,2,4),imshow(J1); title('拉普拉斯算子锐化图像');
 
 
 
 
 
直方图和直方图均衡化：

代码：I=imread('cameraman.tif');subplot(2,2,1);%画子图imshow(I);title('原图');num=zeros(1,256);newnum=zeros(1,256);[m,n]=size(I);for i=1:m   for j=1:n     num(I(i,j)+1)= 1+ num(I(i,j)+1);%统计各像素的总个数   endendx=0:255;y=num/(m*n);%求出各像素点的百分比subplot(2,2,2);bar(x,y,0);xlabel('灰度值');ylabel('概率');title('原图的直方图');for i=2:256   y(i)=y(i)+y(i-1); %计算累计直方图分布endy=y*256; %找出直方图均衡化后原来的像素点所对应的新像素点y=round(y);%四舍五入for i=1:m   for j=1:n     I(i,j)= y(I(i,j)+1)-1;%更改原图成为直方图均衡化后的图     newnum(I(i,j)+1)= 1+ newnum(I(i,j)+1);%统计新图各像素点的个数   endendy2=newnum/(m*n);%求出各像素点的百分比subplot(2,2,3);%画子图imshow(I);title('直方图均衡化后的效果');subplot(2,2,4);bar(x,y2,0);xlabel('灰度值');ylabel('概率');title('直方图均衡化后的直方图');  
 
 
 
 
 
 
 
理想的滤波器:
 

理想低通滤波器（ILPF）:  d=10

代码：I=imread('cameraman.tif');J=imnoise(I,'salt & pepper',0.1);f=double(J);subJ=fftshift(fft2(f));%空间域到频率域[m,n]=size(subJ);for i=1:m   for j=1:n      a=1;      if(sqrt((i-m/2)^2+(j-n/2)^2)>10)        a=0;       end     subJ(i,j)=subJ(i,j)*a;   endendresJ=uint8(real(ifft2(subJ)));%频率域到空间域imshow(resJ,[]);
 
 
 
 
 
 
 
理想高通滤波器（IHPF） d=10

代码：I=imread('cameraman.tif');J=imnoise(I,'salt & pepper',0.1);f=double(J);subJ=fftshift(fft2(f));%空间域到频率域[m,n]=size(subJ);for i=1:m   for j=1:n      a=1;      if(sqrt((i-m/2)^2+(j-n/2)^2)<=10)        a=0;       end     subJ(i,j)=subJ(i,j)*a;   endendresJ=uint8(real(ifft2(subJ)));%频率域到空间域imshow(resJ,[]);
 
 
 
 
 
 
 
 
高斯低通滤波器（GLPF）：

代码：I=imread('cameraman.tif');J1=imnoise(I,'salt & pepper',0.1); f=double(J1);subJ=fftshift(fft2(f));%空间域到频率域[m,n]=size(subJ);for i=1:m   for j=1:n     a=exp(-((i-m/2)^2+(j-n/2)^2)/(2*100)); %d=10      subJ(i,j)=subJ(i,j)*a;   endendresJ=uint8(real(ifft2(subJ)));%频率域到空间域imshow(resJ,[]);
 
 
 
 
 
 
 
 
 
 
 
高斯高通滤波器（GHPF）：

代码：I=imread('cameraman.tif');J1=imnoise(I,'salt & pepper',0.1); f=double(J1);subJ=fftshift(fft2(f));%空间域到频率域[m,n]=size(subJ);for i=1:m   for j=1:n     a=1-exp(-((i-m/2)^2+(j-n/2)^2)/(2*100)); %d=10     subJ(i,j)=subJ(i,j)*a;   endendresJ=uint8(real(ifft2(subJ)));%频率域到空间域imshow(resJ,[]);
      
 
提取红色区域：
I=imread('greens.jpg');subplot(1,2,1);imshow(I);title('原始图像');[m,n,l]=size(I);b=double(I)./255; r=0.1765;a=[0.6863,0.1608,0.1922];for i=1:m   for j=1:n      dis=(b(i,j,1)-a(1))^2+(b(i,j,2)-a(2))^2+(b(i,j,3)-a(3))^2;         if(dis>r^2)              I(i,j,1)=127;              I(i,j,2)=127;              I(i,j,3)=127;         end   endendsubplot(1,2,2);imshow(I,[]);title('提取后');