图像处理matlab程序实例

程序实例

1旋转:

x=imread('d:\MATLAB7\work\flower.jpg');

y=imrotate(x,200,'bilinear','crop');

subplot(1,2,1);

imshow(x);

subplot(1,2,2);

imshow(y)

2.图像的rgb

clear [x,map]=imread('D:\Program

Files\MATLAB\R2012a\bin\shaohaihe\shh1.jpg');y=x(90:95,90:95);imshow(y)R=x(90:95,90:95,1);G=x(90:95,90:95,2);B=x(90:95,90:95,3);R,G,B

3.加法运算clear I=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\shh3.jpg');J=imnoise(I,'gaussian',0,0.02);%向图片加入高斯噪声subplot(1,2,1),imshow(I);%显示图片subplot(1,2,2),imshow(J);K=zeros(242,308);%产生全零的矩阵，大小与图片的大小一样for i=1:100%循环100加入噪声J=imnoise(I,'gaussian',0,0.02);J1=im2double(J);K=K+J1;end K=K/100;

figure,imshow(K);save

4.减法

clear

I=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\shao.jpg'); J=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\shao1.jpg'); K=imsubtract(I,J);%实现两幅图相减

K1=255-K;%将图片求反显示

figure;imshow(I);

title('有噪声的图');

figure;imshow(J);

title('原图');

figure;imshow(K1);

title('提取的噪声');

save

5.图像的乘法

H=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\shao.jpg'); I=immultiply(H,1.2);将此图片乘以1.2

J=immultiply(H,2);

subplot(1,3,1),imshow(H);

title('原图');

subplot(1,3,2),imshow(I);

title('·放大1.2');

subplot(1,3,3),imshow(J);

title('放大2倍');

6除法运算

moon=imread('moon.tif');

I=double(moon);

J=I*0.43+90;

K=I*0.1+90;

L=I*0.01+90;

moon2=uint8(J);

moon3=uint8(K);

moon4=uint8(L);

J=imdivide(moon,moon2);

K=imdivide(moon,moon3);

L=imdivide(moon,moon4);

subplot(2,2,1),imshow(moon);

subplot(2,2,2),imshow(J,[]);

subplot(2,2,3),imshow(K,[]);

subplot(2,2,4),imshow(L,[]);

7.图像的缩放

clear

I=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\shao1.jpg'); figure,imshow(I);

scale=0.5;

J=imresize(I,scale);

figure,imshow(J);

8.图像的旋转

(1)I=imread('D:\Program

Files\MATLAB\R2012a\bin\shaohaihe\shao1.jpg');

figure,imshow(I);

theta=30;

K=imrotate(I,theta);%Try varying the angle,theta.

figure,imshow(K)

（2）

clear

I=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\shao1.jpg'); J=imrotate(I,60,'bilinear');

K=imrotate(I,60,'bilinear','crop');

subplot(1,3,1),imshow(I);

subplot(1,3,2),imshow(J);

subplot(1,3,3),imshow(K);

1.傅里叶变换

clear

I=zeros(256,256);

I(28:228,108:148)=1;

imshow(I)

求原始图像的傅里叶频谱

J=fft2(I);

F=abs(J);

J1=fftshift(F);figure

imshow(J1,[5,50]

clear

I=zeros(256,256);

I(28:228,108:148)=1;

%对原始图像进行旋转

J=imrotate(I,315,’bilinear’,’crop’); figure

imshow(J)

求原始图像的傅里叶频谱

J1=fft2(J);

F=abs(J1);

J2=fftshift(F);figure

imshow(J2,[5,50])

1.图象的FFT变换

clc;

I=imread('005.bmp');

subplot(1,2,1)

imshow(I);

title('原图');

subplot(1,2,2)

imhist(I);

title('直方图');

colorbar;

J=fft2(I);

figure;

subplot(1,2,1)

imshow(J);

title('FFT变换结果');

subplot(1,2,2)

K=fftshift(J);

imshow(K);

title('零点平移');

figure;

imshow(log(abs(K)),[]),colormap(jet(64)),colorbar; title('系数分布图');

2.图象的DCT变换

RGB=imread('005.bmp');

figure;

subplot(1,2,1)

imshow(RGB);

title('彩色原图');

a=rgb2gray(RGB);

subplot(1,2,2)

imshow(a);

title('灰度图');

figure;

b=dct2(a);

imshow(log(abs(b)),[]),colormap(jet(64)),colorbar; title('DCT变换结果');

figure;

b(abs(b)<10)=0;

%idct

c=idct2(b)/255;

imshow(c);

title('IDCT变换结果');

3.小波变换

clc

close all

clear

a=imread('005.bmp');

subplot(1,2,1);

imshow(a);

title('原始图象');

I=rgb2gray(a);

subplot(1,2,2);

imshow(I);

title('原始图象的灰度图');

%进行二维小波变换

[a,b]=wavedec2(I,2,'bior3.7');

%提取各层低频信息

figure;

c=appcoef2(a,b,'bior3.7',1);

subplot(1,2,1);

imshow(c,[]);

title('一层小波变换结果');

d=appcoef2(a,b,'bior3.7',2);

subplot(1,2,2);

imshow(d,[]);

title('二层小波变换结果');

2.尺度变换

clc;

clear;

I=zeros(256,256);

I(8:248,110:136)=5;

figure(1)

imshow(I)

a=0.1;

J3=fft2(I);

F2=abs(J3);

J4=fftshift(F2);

figure(2)

imshow(J4,[5,30])

%乘以比例尺度

for i=1:256

for j=1:256

I(i,j)=I(i,j)*a;

end

end

J2=fft2(I);

F1=abs(J2);

J3=fftshift(F1);

figure(3)

imshow(J3,[5,30])

3傅里叶变换实例

（1）乘以指数将图像亮度整体变暗，并求其中心移到零点的频谱图clear

I=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\shh3.jpg'); figure(1);

imshow(I)

P=I*exp(-1);

figure(2);

imshow(P)

P1=fftshift(fft2(P));

figure(3);

imshow(log(abs(P1)),[8,10])

（2）加入高斯噪声求频谱图

I=imread('D:\Program

Files\MATLAB\R2012a\bin\shaohaihe\shao1.jpg');

I=imnoise(I,'gaussian',0,0.01);

figure;

imshow(I);

P=fftshift(fft2(I));

figure;

imshow(log(abs(P),[8,10])

（3）求相位谱和幅值谱

I=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\shao.jpg'); figure(1)

imshow(real(I));

I=I(:,:,3);

fftI=fft2(I);

sfftI=fftshift(fftI);

RRfdp1=real(sfftI);

IIfdp1=imag(sfftI);

a=sqrt(RRfdp1.^2+IIfdp1.^2);

a=(a-min(min(a)))/(max(max(a))-min(min(a)))*225;

figure(2)

imshow(real(a));

b=angle(fftI);

figure(3)

imshow(real(b));

theta=30;

RR1=a*cos(theta);

II1=a*sin(theta);

fftI1=RR1+i.*II1;

C=ifft2(fftI1)*255;

figure(4)

imshow(real(C));

MM=150;

RR2=MM*cos(angle(fftI));

II2=MM*sin(angle(fftI));

fftI2=RR2+i.*II2;

D=ifft2(fftI2);

fogure(5)

imshow(real(D));

图像增强

1.反转变换：

img1=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\shao.jpg'); figure,imshow(img1);title('original image');

img2=imcomplement(img1);

figure,imshow(img2);title('negative mage');

2.基于直方图处理的图像增强

I=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\zhinv.jpg'); I=rgb2gray(I);

K=16;

H=histeq(I,K);

figure,subplot(2,2,1),imshow(I,[]);

subplot(2,2,2),imshow(H,[]);

subplot(2,2,3),hist(double(I),16);

subplot(2,2,4),hist(double(H),16);

（2）在matlab环境中，程序首先读取图像，然后调用直方图函数，设置相关参数，再输出处理后的图像。

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

（3）灰度均衡

I=imread('cameraman.tif');%读取图像

subplot(2,2,1),imshow(I)%输出图像

title('原始图像')%在原始图像中加标题

subplot(2,2,3),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

a=histeq(I,256);%直方图均衡化，灰度级为256

subplot(2,2,2),imshow(a)%输出均衡化后图像

title('均衡化后图像')%在均衡化后图像中加标题

subplot(2,2,4),imhist(a)%输出均衡化后直方图

title('均衡化后图像直方图')%在均衡化后直方图上加标题

（4）均值滤波

在matlab环境中，程序首先读取图像，然后调用图像增强（均值滤波）函数，设置相关

参数，再输出处理后的图像。

I=imread('cameraman.tif');

figure,imshow(I);

J=filter2(fspecial(‘average’,3),I)/255;figure,imshow(J);

（5）中值滤波

在matlab环境中，程序首先读取图像，然后调用图像增强（中值滤波）函数，设置相关参数，再输出处理后的图像。

I=imread('cameraman.tif');

figure,imshow(I);

J=medfilt2(I,[5,5]);

figure,imshow(J);

图像边缘检测

在matlab环境中，程序首先读取图像，然后调用图像边缘检测（Sobel、Prewitt、Log 边缘算子）函数，设置相关参数，再输出处理后的图像。

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);

2i=imread('eight.tif');

figure;

subplot(2,2,1);

imshow(i);

title('原始图像');

subplot(2,2,3);

imshow(i);

title('原始图像');

i1=edge(i,'sobel');

subplot(2,2,2);

imshow(i1);

title('sober方法提取的边缘');

i2=edge(i,'log');

subplot(2,2,4);

imshow(i2);

title('Laplacian-Gaussian方法提取的边缘');

3.H=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\hua.jpg'); I=rgb2gray(H);

BW1=edge(I,'sobel');

BW2=edge(I,'prewitt');

BW3=edge(I,'roberts');

BW4=edge(I,'log');

figure

imshow(I)

figure

subplot(2,2,1)

imshow(BW1)

xlabel('soble')

subplot(2,2,2)

imshow(BW2)

xlabel('prewitt')

subplot(2,2,3)

imshow(BW3)

xlabel('roberts')

subplot(2,2,4)

imshow(BW4)

xlabel('log')

4.H=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\hua.jpg'); I=rgb2gray(H);

subplot(1,3,1);

imshow(I);

title('hua');

K=fspecial('laplacian',0.7);

K1=filter2(K,I)/100;

subplot(1,3,2);

imshow(K1);

title('laplacian');

L=fspecial('sobel');

L1=filter2(L,I)/200;

subplot(1,3,3);

imshow(L1);

title('sibel');

2.图像平滑5x5淋浴领域平滑

img=rgb2gray(imread('D:\ProgramFiles\MATLAB\R2012a\bin\shaohaihe\hua. jpg'));

figure;imshow(img);

img_noise=double(imnoise(img,'gaussian',0.06));高斯噪声

figure,imshow(img_noise,[]);

img_smoothed=imfilter(img_noise,fspecial('average',5));

figure;imshow(img_smoothed,[])

3.同以图像加入不同噪声的直方图分布

clear

I=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\hua.jpg'); figure,imshow(I),figure,hist(double(I),10);

J=imnoise(I,'gaussian',0.05);

figure,imshow(J),figure,hist(double(J),10)

J1=imnoise(I,'speckle',0.05);

figure,imshow(J1),figure,hist(double(J1),10)

J2=imnoise(I,'salt&pepper',0.05);

figure,imshow(J2),hist(double(J2),10)

4.各种均值滤波方法

img=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\hua.jpg'); figure;imshow(img);

img_noise=double(imnoise(img,'gaussian',0.006));

figure,imshow(img_noise,[]);

img_mean=imfilter(img_noise,fspecial('average',3));

figure;imshow(img_mean,[]);title('de-noise by mean filter');

imag_mean=exp(imfilter(log(img_noise),fspecial('average',5))); figure;imshow(img_mean,[]),title('de-noise by mean filter');

Q=-1.5;

img_mean=imfilter(img_noise.^(Q+1),fspecial('average',3))./imfilter(i mg_noise.^Q,fspecial('average',3));

figure;imshow(img_mean,[]);title('de-noise by mean filter');

Q=1.5;

img_mean=imfilter(img_noise.^(Q+1),fspecial('average',3))./imfilter(i mg_noise.^Q,fspecial('average',3));

figure;imshow(img_mean,[]);title('de-noise by mean filter');

5.标准的均值，中值滤波

img=rgb2gray(imread('D:\ProgramFiles\MATLAB\R2012a\bin\shaohaihe\qie. jpg'));

figure;imshow(img);

img_noise=double(imnoise(img,'salt&pepper',0.06));

figure,imshow(img_noise,[]);

img_mean=imfilter(img_noise,fspecial('average',5));

figure;imshow(img_mean,[]);title('de-noise by mean filter');

img_median=medfilt2(img_noise);

figure;imshow(img_median,[]);title('de-noise by median filter'); img_median2=medfilt2(img_noise);

figure;imshow(img_median2,[]);title('de-noise by median filter');

图像分割

1．OTSU阀值

I=imread('D:\Program

Files\MATLAB\R2012a\bin\shaohaihe\lena.jpg');

[width,height]=size(I);

%otsu algorithm

level=graythresh(I);

BW=im2bw(I,level);

figure;

imshow(BW)

2.全局阀值

I=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\lena.jpg'); [width,height]=size(I);

%global threshold

for i=1:width

for j=1:height

if(I(i,j)<80)

B W1(i,j)=1;

else

B W1(i,j)=1;

end

end

end

figure;

imshow(BW1)

3.迭代求阀值

I=imread('D:\Program Files\MATLAB\R2012a\bin\shaohaihe\lena.jpg'); [width,height]=size(I);

%μü′ú?ó·§?μ

I=double(I);

T=(min(I(:))+max(I(:)))/2;

done=false;

i=0;

while~done

r1=find(I<=T);

r2=find(I>T);

Tnew=(mean(I(r1))+mean(I(r2)))/2;

done=abs(Tnew-T)<1;

T=Tnew;

i=i+1;

end

I(r1)=0;

I(r2)=1;

figure;

imshow(I)