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;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];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 1] subplot(2,2,3,imshow(J;title('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 1] subplot(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('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=histeq(I;figure;subplot(2,2,1;imshow(I1;subplot(2,2,2;imhist(I1;5.线性平滑滤波器用MA TLAB实现领域平均法抑制噪声程序:I=imread('xian.bmp';subplot(231imshow(Ititle('原始图像'I=rgb2gray(I;I1=imnoise(I,'salt & pepper',0.02;subplot(232imshow(I1title('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MA TLAB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];grid on; %显示网格线axis on; %显示坐标系I1=im2bw(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(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MA TLAB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;title('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MA TLAB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MA TLAB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(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;hold on;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];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;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(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(linesxy=[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-lines(k.point2;if(len>max_lenmax_len=len;xy_long=xy;endendplot(xy_long(:,1,xy_long(:,2,'LineWidth',2,'Color','cyan';13.直方图阈值法用MA TLAB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(1,256; %预创建存放灰度出现概率的向量for k=0:255GP(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; % 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MA TLAB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(1,2,2,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; imshow(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; imshow(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MA TLAB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; %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;title('灰度图像';axis([50,250,50,200];axis on; %显示坐标系se=strel('disk',1;I2=imopen(I1,se; %开启操作I3=imclose(I1,se; %闭合操作subplot(3,2,3,imshow(I2;title('开启运算后图像';axis([50,250,50,200];axis on; %显示坐标系subplot(3,2,4,imshow(I3;title('闭合运算后图像';axis([50,250,50,200];axis on; %显示坐标系se=strel('disk',1;I4=imopen(I1,se;I5=imclose(I4,se;subplot(3,2,5,imshow(I5; %开—闭运算图像title('开—闭运算图像';axis([50,250,50,200];axis on; %显示坐标系I6=imclose(I1,se;I7=imopen(I6,se;subplot(3,2,6,imshow(I7; %闭—开运算图像title('闭—开运算图像'; axis([50,250,50,200];axis on; %显示坐标系19.形态学边界提取利用MATLAB实现如下:I=imread('xian.bmp'; %载入图像subplot(1,3,1,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(1,3,2,imshow(I1;title('二值化图像';axis([50,250,50,200];grid on; %显示网格线axis on; %显示坐标系I2=bwperim(I1; %获取区域的周长subplot(1,3,3,imshow(I2; title('边界周长的二值图像';axis([50,250,50,200];grid on;axis on;20.形态学骨架提取利用MATLAB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;I1=im2bw(I;subplot(2,2,2,imshow(I1; title('二值图像';axis([50,250,50,200];axis on;I2=bwmorph(I1,'skel',1; subplot(2,2,3,imshow(I2; title('1次骨架提取';axis([50,250,50,200];axis on; I3=bwmorph(I1,'skel',2; subplot(2,2,4,imshow(I3; title('2 次骨架提取'; axis([50,250,50,200]; axis on; 21.直接提取四个顶点坐标 I = imread('xian.bmp'; I = I(:,:,1; BW=im2bw(I; figure imshow(~BW [x,y]=getpts。