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(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);
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 _disibledevent=>
subplot(2,2,2),imshow(I1);
title('灰度图像');
axis([50,250,50,200]);
axis _disibledevent=>
subplot(2,2,3),imshow(J);
title('线性变换图像[0.1 0.5]');
axis([50,250,50,200]);
grid _disibledevent=>
subplot(2,2,4),imshow(K);
title('线性变换图像[0.3 0.7]');
axis([50,250,50,200]);
grid _disibledevent=>
I1=rgb2gray(I);
subplot(1,2,1),imshow(I1);
title(' 灰度图像');
axis([50,250,50,200]);
grid _disibledevent=>
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 _disibledevent=>
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('lena.jpg);
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('lena.jpg.');
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 _disibledevent=>
subplot(2,2,2),imshow(I1);
title('二值图像');
axis([50,250,50,200]);
grid _disibledevent=>
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 _disibledevent=>
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 _disibledevent=>
subplot(2,3,1);
imshow(I);
title('原始图像');
axis([50,250,50,200]);
grid _disibledevent=>
subplot(2,3,2);
imshow(I1);
title('二值图像');
axis([50,250,50,200]);
grid _disibledevent=>
figure;
subplot(2,3,3);
imshow(I2);
title('roberts算子分割结果');
axis([50,250,50,200]);
grid _disibledevent=>
subplot(2,3,4);
imshow(I3);
title('sobel算子分割结果');
axis([50,250,50,200]);
grid _disibledevent=>
subplot(2,3,5);
imshow(I4);
title('Prewitt算子分割结果 ');
axis([50,250,50,200]);
grid _disibledevent=>
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）
clc
clear all
I=imread('xian.bmp');
figure
imshow(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
figure
imshow(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
figure
imshow(I1);
hold _disibledevent=>
rotI=rgb2gray(I);
subplot(2,2,1);
imshow(rotI);
title('灰度图像');
axis([50,250,50,200]);
grid _disibledevent=>
subplot(2,2,2);
imshow(BW);
title('prewitt算子边缘检测 后图像');
axis([50,250,50,200]);
grid _disibledevent=>
subplot(2,2,3);
imshow(H,[],'XData',T,'YData',R,'InitialMagnification','fit');
title('霍夫变换图');
xlabel('\theta'),ylabel('\rho');
axis _disibledevent=>
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 _disibledevent=>
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-lines(k).point2);
if(len>max_len)
max_len=len;
xy_long=xy;
end
end
plot(xy_long(:,1),xy_long(:,2),'LineWidth',2,'Color','cyan');
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