西电数字图像处理上机报告

1、数字图像处理上机报告实验一一、实验目的1熟悉有关数字图像处理的MATLAB基本操作和编写；2掌握数字图像的基本读写、显示与保存；3能够利用MATLAB工具箱完成图像处理的基本运算；二、实验内容1（1）读取图像并显示真彩色图像house.tiff；（2）读取图像并显示索引色图像canoe.tif，并转换为灰度图像；（3）读取图像并显示灰度图像rice.png，并转换为二进制图像；（4）检测内存中的图像和保存图像；2查看不同分辨率下图像lena.tiff的显示结果；3对彩色图像Peppers.png观看GRB通道各单通道图像；4实现马赫带效应并画出灰度直方图。程序如下：clear allclose

2、 allclc%Task1%读取图像并显示真彩色图像%读入图像img_house=imread('house.tiff');figure('NumberTitle', 'off', 'Name', '真彩House');imshow(img_house)%Task2%读取图像并显示索引色图像，并转换为灰度图像；%读入图像img_canoe,MAP_canoe=imread('canoe.tif');figure('NumberTitle', 'off', 'N

3、ame', '索引Canoe');imshow(img_canoe,MAP_canoe)gray_canoe = ind2gray(img_canoe,MAP_canoe);figure('NumberTitle', 'off', 'Name', '灰度Canoe');imshow(gray_canoe);%Task3%读取图像并显示灰度图像，并转换为二进制图像%读入图像img_rice=imread('rice.png');figure('NumberTitle', 

4、9;off', 'Name', '灰度Rice');imshow(img_rice)bw_rice = im2bw(img_rice);figure('NumberTitle', 'off', 'Name', '二进制Rice');imshow(bw_rice);%Task4%读取图像-保存图像-重新读取保存的图像%读入图像img_rice=imread('rice.png');figure('NumberTitle', 'off', '

5、;Name', 'Read Image Rice');imshow(img_rice)imwrite(img_rice,'RiceImg_Write','TIFF');img_rice_Rre=imread('RiceImg_Write','TIFF');figure('NumberTitle', 'off', 'Name', 'Reread Rice');imshow(img_rice_Rre);%Task4%读取图像并显示真彩色图像%读入图

6、像img_lena=imread('lena.tiff');figure('NumberTitle', 'off', 'Name', 'Lena');imshow(img_lena)small_lena = imresize(img_lena, 0.1);%图像缩放figure('NumberTitle', 'off', 'Name', 'Small Lena');imshow(small_lena)big_lena = imresize(img_le

7、na, 0.7);%图像缩放figure('NumberTitle', 'off', 'Name', 'Big Lena');imshow(big_lena)%Task5%对彩色图像Peppers.png观看GRB通道各单通道图像%读入图像I = imread('Peppers.png');R=I;R(:,:,2 3)=0;G=I;G(:,:,1 3)=0;B=I;B(:,:,1 2)=0;figure,imshow(R);figure,imshow(G);figure,imshow(B);%Task4%实现马赫带

8、效应并画出灰度直方图imsize_x=300;%图像大小（正方形）imsize_y=300;n=7;%马赫带个数img_machbands=zeros(imsize_x,imsize_y);for i=1:n img_machbands(:,fix(imsize_y*(i-1)/n)+1:fix(imsize_y*i/n)=255*(i-1)/n;endimg_machbands=uint8(img_machbands);figure('NumberTitle', 'off', 'Name', 'Mach Bands');imsh

9、ow(img_machbands);figure('NumberTitle', 'off', 'Name', 'Mach Bands Hist');imhist(img_machbands);结果如下：实验二一、实验目的1熟悉有关MATLAB工具箱中提供的图像变换函数；2掌握傅立叶变换和小波变换等常用的正交变换函数；二、实验内容1对图像lena.png进行傅立叶变换和离散余弦变换，并分别求出其逆变换后重构图像的均方误差；2实现图像lena.png的两层小波分解，观察分解系数并重构，求重构图像误差值；3观察图像IM1.BMP经小波分

10、解系数的方向性，实现三层小波分解，分别把HL,LH,HH子带置零和重构，观察重构图像跟原始图像的差异。程序如下：clear allclose allclc% FFT% 傅立叶变换和离散余弦变换，并分别求出其逆变换后重构图像的均方误差%读入图像img_lena=imread('lena.png');figure('NumberTitle', 'off', 'Name', 'Lena');imshow(img_lena)%FFT变换%img_lena_fft=fft2(img_lena);spectrum = ffts

11、hift(abs(img_lena_fft);spectrum = 255*spectrum/max(spectrum(:);figure('NumberTitle', 'off', 'Name', 'Lena Spectrum');spectrum=uint8(spectrum);imshow(spectrum);%iFFT变换%img_lena_ifft=real(ifft2(img_lena_fft);%进行反变换img_lena_ifft=uint8(img_lena_ifft);figure('NumberTit

12、le', 'off', 'Name', 'Lena iFFT Recovery');imshow(img_lena_ifft);%图像还原误差%deviation=norm(double(img_lena_ifft-img_lena)2;text(100,100,num2str(deviation),'FontSize',18,'Color','b')%DCT变换%I = img_lena;img_lena_dct = dct2(I);figure('NumberTitle'

13、, 'off', 'Name', 'Lena DCT Spectrum');imshow(log(abs(img_lena_dct),), colormap(jet), colorbar%iDCT变换% % J(abs(J)<10) = 0;%去掉部分信息img_lena_idct = idct2(img_lena_dct);figure('NumberTitle', 'off', 'Name', 'Lena iDCT Recovery');imshow(img_lena_id

14、ct,0 255)%图像还原误差%deviation_dct=norm(double(img_lena_idct-double(img_lena)2;text(100,100,num2str(deviation_dct),'FontSize',18,'Color','b')% Wavelet% 小波变换与反变换,实现图像lena.png的两层小波分解，观察分解系数并重构，求重构图像误差值 Lo_D,Hi_D,Lo_R,Hi_R = wfilters('db1');%生成小波变换以及反变换所需的低通高通滤波器N=2;%2层小波变换C

15、,S = wavedec2(img_lena,N,Lo_D,Hi_D);%小波变换分层分开显示ca1=wcodemat(appcoef2(C,S,'db1',1),255);ch1=wcodemat(detcoef2('h',C,S,1),255);cv1=wcodemat(detcoef2('v',C,S,1),255);cd1=wcodemat(detcoef2('d',C,S,1),255);% c1=uint8(ca1,ch1;cv1,cd1);% figure('NumberTitle', 'of

16、f', 'Name', 'Lena Wavelet Level 1');% imshow(c1);ca2=wcodemat(appcoef2(C,S,'db1',2),255);ch2=wcodemat(detcoef2('h',C,S,2),255);cv2=wcodemat(detcoef2('v',C,S,2),255);cd2=wcodemat(detcoef2('d',C,S,2),255);% c2=uint8(ca2,ch2;cv2,cd2);% figure('Num

17、berTitle', 'off', 'Name', 'Lena Wavelet Level 2');% imshow(c2);%小波变换两层结果同时显示L2 = ca2,ch2;cv2,cd2;k=S(2,1)*2-S(3,1);ch1 = padarray(ch1,k k,1,'post');%保护原有数据矩阵cv1 = padarray(cv1,k k,1,'post');cd1 = padarray(cd1,k k,1,'post');L = L2,ch1; cv1,cd1;%将第二层

18、小波变换结果添加到第一层小波变换结果上figure('NumberTitle', 'off', 'Name', 'Lena Wavelet L2');imshow(L,'InitialMagnification',100);%小波反变换% img_iwavelet = waverec2(C,S,Lo_R,Hi_R);figure('NumberTitle', 'off', 'Name', 'Lena iWavelet Recovery');imshow

19、(uint8(img_iwavelet);%图像还原误差%deviation_wavelet=norm(double(img_iwavelet-double(img_lena)2;text(100,100,num2str(deviation_wavelet),'FontSize',18,'Color','b')% Wavelet % 观察图像IM1.BMP经小波分解系数的方向性，实现三层小波分解，分别把HL,LH,HH子带置零和重构，观察重构图像跟原始图像的差异。%读入图像img_im1=imread('IM1.BMP');fig

20、ure('NumberTitle', 'off', 'Name', 'TM1 Source Image');imshow(img_im1)C,S=wavedec2(double(img_im1),3,'db1');ca3 = appcoef2(C,S,'db1',3);ch3 = detcoef2('h',C,S,3);cv3 = detcoef2('v',C,S,3);cd3 = detcoef2('d',C,S,3);ch2 = detcoef2(

21、'h',C,S,2);cv2 = detcoef2('v',C,S,2);cd2 = detcoef2('d',C,S,2);ch1 = detcoef2('h',C,S,1);cv1 = detcoef2('v',C,S,1);cd1 = detcoef2('d',C,S,1);% L3 = ca3*4,ch3*4;cv3*4,cd3*4;L3 = ca3,ch3;cv3,cd3;k=S(2,1)*2-S(3,1);ch2 = padarray(ch2,k k,1,'post');

22、cv2 = padarray(cv2,k k,1,'post');cd2 = padarray(cd2,k k,1,'post');% L2= L3,ch2*4; cv2*4,cd2*4;L2= L3,ch2; cv2,cd2;k=S(2,1)*4-S(4,1);ch1 = padarray(ch1,k k,1,'post');cv1 = padarray(cv1,k k,1,'post');cd1 = padarray(cd1,k k,1,'post');% L = L2,ch1*4; cv1*4,cd1*4;L

23、 = L2,ch1; cv1,cd1;figure('NumberTitle', 'off', 'Name', 'Lena Wavelet L3');imshow(L,'InitialMagnification',100);%LH置零%ca3 = appcoef2(C,S,'db1',3);ch3 = detcoef2('h',C,S,3);cv3 = detcoef2('v',C,S,3);cd3 = detcoef2('d',C,S,3);ch2 =