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, Name, 索引Canoe);imshow(img_canoe,MAP_canoe)gray_canoe = ind2gray(img_canoe,MAP_canoe);f

3、igure(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, 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

4、(rice.png);figure(NumberTitle, off, 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%读入图像img_lena=imread(lena.tiff);figure(NumberTitle, off, Name, L

5、ena);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_lena, 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)

6、=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(im

7、g_machbands);figure(NumberTitle, off, Name, Mach Bands);imshow(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

8、误差值；3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。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 = fftshift(abs(img_lena_fft);spectru

9、m = 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(NumberTitle, off, Name, Lena iFFT Recovery);imshow(img_lena_ifft);%图像还原误差%deviation=

10、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, 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_len

11、a_dct);figure(NumberTitle, off, Name, Lena iDCT Recovery);imshow(img_lena_idct,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

12、);%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,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, off, Name, Lena Wavel

13、et 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(NumberTitle, 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

14、;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);figur

15、e(NumberTitle, off, Name, Lena iWavelet Recovery);imshow(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=imr

16、ead(IM1.BMP);figure(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(h,C,S,2);cv2 = detcoef2(v,C,S,2);cd2 = detcoef2(d,C,S,2);ch1 = detcoef2(h,

17、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);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(c

18、h1,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 = 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);c