matlab实用源码

MATLAB实用源代码2009-09-3008:311.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);subplot(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]);axison;%显示坐标系I1=rgb2gray(I);subplot(2,2,2),imshow(I1);title('灰度图像');axis([50,250,50,200]);axison;%显示坐标系J=imadjust(I1,[0.10.5],[]);%局部拉伸，把[0.10.5]内的灰度拉伸为[01]subplot(2,2,3),imshow(J);title('线性变换图像[0.10.5]');axis([50,250,50,200]);gridon;%显示网格线axison;%显示坐标系K=imadjust(I1,[0.30.7],[]);%局部拉伸，把[0.30.7]内的灰度拉伸为[01]subplot(2,2,4),imshow(K);title('线性变换图像[0.30.7]');axis([50,250,50,200]);gridon;%显示网格线axison;%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('灰度图像');axis([50,250,50,200]);gridon;%显示网格线axison;%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]);gridon;%显示网格线axison;%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=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(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);title('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subplot(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]);gridon;%显示网格线axison;%显示坐标系I1=im2bw(I);subplot(2,2,2),imshow(I1);title('二值图像');axis([50,250,50,200]);gridon;%显示网格线axison;%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]);gridon;%显示网格线axison;%显示坐标系h=[010,1-41,010];%拉普拉斯算子J1=conv2(I1,h,'same');%卷积运算subplot(2,2,4),imshow(J1);title('拉普拉斯算子锐化图像');axis([50,250,50,200]);gridon;%显示网格线axison;%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]);gridon;%显示网格线axison;%显示坐标系I1=im2bw(I);subplot(2,3,2);imshow(I1);title('二值图像');axis([50,250,50,200]);gridon;%显示网格线axison;%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]);gridon;%显示网格线axison;%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]);gridon;%显示网格线axison;%显示坐标系I4=edge(I1,'Prewitt');subplot(2,3,5);imshow(I4);title('Prewitt算子分割结果');axis([50,250,50,200]);gridon;%显示网格线axison;%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=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,'canny');subplot(2,2,3);imshow(I2);title('canny算子分割结果');11.边界跟踪（bwtraceboundary函数）clcclearallI=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(I1);%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);holdon;plot(contour(:,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]);gridon;axison;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]);gridon;axison;[H,T,R]=hough(BW);subplot(2,2,3);imshow(H,[],'XData',T,'YData',R,'InitialMagnification','fit');title('霍夫变换图');xlabel('\theta'),ylabel('\rho');axison,axisnormal,holdon;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(rotI);title('霍夫变换图像检测');axis([50,250,50,200]);gridon;axison;holdon;max_len=0;fork=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