数字图像处理matlab代码.docx

1、数字图像处理matlab代码MATLAB实用源代码图像读取及灰度变换I=imread(cameraman.tif);%读取图像subplot(1,2,1),imshow(I) %输出图像title(原始图像) %在原始图像中加标题subplot(1,2,2),imhist(I) %输出原图直方图title(原始图像直方图) %在原图直方图上加标题图像旋转I = imread(cameraman.tif);figure,imshow(I);theta = 30;K = imrotate(I,theta); % Try varying the angle, theta.figure, imshow

2、(K)边缘检测I = imread(cameraman.tif);J1=edge(I,sobel);J2=edge(I,prewitt);J3=edge(I,log);subplot(1,4,1),imshow(I);subplot(1,4,2),imshow(J1);subplot(1,4,3),imshow(J2);subplot(1,4,4),imshow(J3);1.图像反转MATLAB 程序实现如下：I=imread(xian.bmp);J=double(I);J=-J+(256-1); %图像反转线性变换H=uint8(J);subplot(1,2,1),imshow(I);sub

3、plot(1,2,2),imshow(H);2.灰度线性变换MATLAB 程序实现如下：I=imread(xian.bmp);subplot(2,2,1),imshow(I);title(原始图像);axis(50,250,50,200);axis on; %显示坐标系I1=rgb2gray(I);subplot(2,2,2),imshow(I1);title(灰度图像);axis(50,250,50,200);axis on; %显示坐标系J=imadjust(I1,0.1 0.5,); %局部拉伸，把0.1 0.5内的灰度拉伸为0 1subplot(2,2,3),imshow(J);tit

4、le(线性变换图像0.1 0.5);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系K=imadjust(I1,0.3 0.7,); %局部拉伸，把0.3 0.7内的灰度拉伸为0 1subplot(2,2,4),imshow(K);title(线性变换图像0.3 0.7);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系3.非线性变换MATLAB 程序实现如下：I=imread(xian.bmp);I1=rgb2gray(I);subplot(1,2,1),imshow(I1);title(

5、 灰度图像);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系J=double(I1);J=40*(log(J+1);H=uint8(J);subplot(1,2,2),imshow(H);title( 对数变换图像);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系4.直方图均衡化MATLAB 程序实现如下：I=imread(xian.bmp);I=rgb2gray(I);figure;subplot(2,2,1);imshow(I);subplot(2,2,2);imhist(I);I1

6、=histeq(I);figure;subplot(2,2,1);imshow(I1);subplot(2,2,2);imhist(I1);5. 线性平滑滤波器用MATLAB实现领域平均法抑制噪声程序：I=imread(xian.bmp);subplot(231)imshow(I)title(原始图像)I=rgb2gray(I);I1=imnoise(I,salt & pepper,0.02);subplot(232)imshow(I1)title( 添加椒盐噪声的图像)k1=filter2(fspecial(average,3),I1)/255; %进行3*3模板平滑滤波k2=filter2

7、(fspecial(average,5),I1)/255; %进行5*5模板平滑滤波k3=filter2(fspecial(average,7),I1)/255; %进行7*7模板平滑滤波k4=filter2(fspecial(average,9),I1)/255; %进行9*9模板平滑滤波subplot(233),imshow(k1);title(3*3 模板平滑滤波);subplot(234),imshow(k2);title(5*5 模板平滑滤波);subplot(235),imshow(k3);title(7*7 模板平滑滤波);subplot(236),imshow(k4);titl

8、e(9*9 模板平滑滤波);6.中值滤波器用MATLAB实现中值滤波程序如下：I=imread(xian.bmp);I=rgb2gray(I);J=imnoise(I,salt&pepper,0.02);subplot(231),imshow(I);title(原图像);subplot(232),imshow(J);title(添加椒盐噪声图像);k1=medfilt2(J); %进行3*3模板中值滤波k2=medfilt2(J,5,5); %进行5*5模板中值滤波k3=medfilt2(J,7,7); %进行7*7模板中值滤波k4=medfilt2(J,9,9); %进行9*9模板中值滤波s

9、ubplot(233),imshow(k1);title(3*3模板中值滤波);subplot(234),imshow(k2);title(5*5模板中值滤波 );subplot(235),imshow(k3);title(7*7模板中值滤波);subplot(236),imshow(k4);title(9*9 模板中值滤波);7.用Sobel算子和拉普拉斯对图像锐化：I=imread(xian.bmp);subplot(2,2,1),imshow(I);title(原始图像);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系I1=im2bw

10、(I);subplot(2,2,2),imshow(I1);title(二值图像);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系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);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系h=0 1 0,1 -4 1,0 1 0; %拉普拉斯算子J1=conv2(I1,h,same); %卷积运算subplot(

11、2,2,4),imshow(J1);title(拉普拉斯算子锐化图像);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系8.梯度算子检测边缘用 MATLAB实现如下：I=imread(xian.bmp);subplot(2,3,1);imshow(I);title(原始图像);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系I1=im2bw(I);subplot(2,3,2);imshow(I1);title(二值图像);axis(50,250,50,200);grid on; %显示网格线

12、axis on; %显示坐标系I2=edge(I1,roberts);figure;subplot(2,3,3);imshow(I2);title(roberts算子分割结果);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系I3=edge(I1,sobel);subplot(2,3,4);imshow(I3);title(sobel算子分割结果);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系I4=edge(I1,Prewitt);subplot(2,3,5);imshow(I4);ti

13、tle(Prewitt算子分割结果 );axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系9.LOG算子检测边缘用 MATLAB程序实现如下：I=imread(xian.bmp);subplot(2,2,1);imshow(I);title(原始图像);I1=rgb2gray(I);subplot(2,2,2);imshow(I1);title(灰度图像);I2=edge(I1,log);subplot(2,2,3);imshow(I2);title(log算子分割结果);10.Canny算子检测边 缘用MATLAB程序实现如下：I=imrea

14、d(xian.bmp);subplot(2,2,1);imshow(I);title(原始图像)I1=rgb2gray(I);subplot(2,2,2);imshow(I1);title(灰度图像);I2=edge(I1,canny);subplot(2,2,3);imshow(I2);title(canny算子分割结果);11.边界跟踪 （bwtraceboundary函数）clcclear allI=imread(xian.bmp);figureimshow(I);title(原始图像);I1=rgb2gray(I); %将彩色图像转化灰度图像threshold=graythresh(I

15、1); %计算将灰度图像转化为二值图像所需的门限BW=im2bw(I1, threshold); %将灰度图像转化为二值图像figureimshow(BW);title(二值图像);dim=size(BW);col=round(dim(2)/2)-90; %计算起始点列坐标row=find(BW(:,col),1); %计算起始点行坐标connectivity=8;num_points=180;contour=bwtraceboundary(BW,row,col,N,connectivity,num_points);%提取边界figureimshow(I1);hold on;plot(cont

16、our(:,2),contour(:,1), g,LineWidth ,2);title(边界跟踪图像);12.Hough变换I= imread(xian.bmp);rotI=rgb2gray(I);subplot(2,2,1);imshow(rotI);title(灰度图像);axis(50,250,50,200);grid on;axis on;BW=edge(rotI,prewitt);subplot(2,2,2);imshow(BW);title(prewitt算子边缘检测 后图像);axis(50,250,50,200);grid on;axis on;H,T,R=hough(BW)

17、;subplot(2,2,3);imshow(H,XData,T,YData,R,InitialMagnification,fit);title(霍夫变换图);xlabel(theta),ylabel(rho);axis on , axis normal, hold on;P=houghpeaks(H,5,threshold,ceil(0.3*max(H(:);x=T(P(:,2);y=R(P(:,1);plot(x,y,s,color,white);lines=houghlines(BW,T,R,P,FillGap,5,MinLength,7);subplot(2,2,4);,imshow(

18、rotI);title(霍夫变换图像检测);axis(50,250,50,200);grid on;axis on;hold on;max_len=0;for k=1:length(lines)xy=lines(k).point1;lines(k).point2;plot(xy(:,1),xy(:,2),LineWidth,2,Color,green);plot(xy(1,1),xy(1,2),x,LineWidth,2,Color,yellow);plot(xy(2,1),xy(2,2),x,LineWidth,2,Color,red);len=norm(lines(k).point1-li

19、nes(k).point2);if(lenmax_len)max_len=len;xy_long=xy;endendplot(xy_long(:,1),xy_long(:,2),LineWidth,2,Color,cyan);13.直方图阈值法用 MATLAB实现直方图阈值法：I=imread(xian.bmp);I1=rgb2gray(I);figure;subplot(2,2,1);imshow(I1);title( 灰度图像)axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系m,n=size(I1); %测量图像尺寸参数GP=zeros(

20、1,256); %预创建存放灰度出现概率的向量for k=0:255 GP(k+1)=length(find(I1=k)/(m*n); %计算每级灰度出现的概率，将其存入GP中相应位置endsubplot(2,2,2),bar(0:255,GP,g) %绘制直方图title(灰度直方图)xlabel(灰度值)ylabel( 出现概率)I2=im2bw(I,150/255);subplot(2,2,3),imshow(I2);title(阈值150的分割图像)axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系I3=im2bw(I,200/255)

21、; %subplot(2,2,4),imshow(I3);title(阈值200的分割图像)axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系14. 自动阈值法：Otsu法用MATLAB实现Otsu算法：clcclear allI=imread(xian.bmp);subplot(1,2,1),imshow(I);title(原始图像)axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系level=graythresh(I); %确定灰度阈值BW=im2bw(I,level);subplot(1,

22、2,2),imshow(BW);title(Otsu 法阈值分割图像)axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系15.膨胀操作I=imread(xian.bmp); %载入图像I1=rgb2gray(I);subplot(1,2,1);imshow(I1);title(灰度图像)axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系se=strel(disk,1); %生成圆形结构元素I2=imdilate(I1,se); %用生成的结构元素对图像进行膨胀subplot(1,2,2);ims

23、how(I2);title( 膨胀后图像);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系16.腐蚀操作MATLAB 实现腐蚀操作I=imread(xian.bmp); %载入图像I1=rgb2gray(I);subplot(1,2,1);imshow(I1);title(灰度图像)axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系se=strel(disk,1); %生成圆形结构元素I2=imerode(I1,se); %用生成的结构元素对图像进行腐蚀subplot(1,2,2);imsh

24、ow(I2);title(腐蚀后图像);axis(50,250,50,200);grid on; %显示网格线axis on; %显示坐标系17.开启和闭合操作用 MATLAB实现开启和闭合操作I=imread(xian.bmp); %载入图像subplot(2,2,1),imshow(I);title(原始图像);axis(50,250,50,200);axis on; %显示坐标系I1=rgb2gray(I);subplot(2,2,2),imshow(I1);title(灰度图像);axis(50,250,50,200);axis on; %显示坐标系se=strel(disk,1);

25、%采用半径为1的圆作为结构元素I2=imopen(I1,se); %开启操作I3=imclose(I1,se); %闭合操作subplot(2,2,3),imshow(I2);title(开启运算后图像);axis(50,250,50,200);axis on; %显示坐标系subplot(2,2,4),imshow(I3);title(闭合运算后图像);axis(50,250,50,200);axis on; %显示坐标系18.开启和闭合组合操作I=imread(xian.bmp); %载入图像subplot(3,2,1),imshow(I);title(原始图像);axis(50,250,50,200);axis on; %显示坐标系I1=rgb2gray(I);subplot(3,2,2),imshow(I1)