《数字图像处理》实验指导书201403.doc

1直方图均衡化clear all; close all % Clear the MATLAB workspace of any variables % and close open figure windows.I = imread(pout.tif); % Reads the sample images pout.tif, and stores it in imshow(I) % an array named I.display the imagefigure, imhist(I) % Create a histogram of the image and display it in % a new figure window.I2,T = histeq(I); % Histogram equalization.figure, imshow(I2) % Display the new equalized image, I2, in a new figure window.figure, imhist(I2) % Create a histogram of the equalized image I2.figure,plot(0:255)/255,T); % plot the transformation curve.imwrite (I2, pout2.png); % Write the newly adjusted image I2 to a disk file named% pout2.png.imfinfo(pout2.png) % Check the contents of the newly written file2直接灰度变换clear all; close allI = imread(cameraman.tif);J = imadjust(I,0 0.2,0.5 1);imshow(I)figure, imshow(J)X,map = imread(forest.tif);figure,imshow(X,map)I2 = ind2gray(X,map);J2 = imadjust(I2,0.5); figure,imshow(I2)figure, imshow(J2)J3 = imadjust(I2,1.5); figure, imshow(J3)help imadjust % Display the imadjust() function information.3空域平滑滤波（模糊、去噪）clear all; close allI = imread(eight.tif);h1 = ones(3,3) / 9;h2 = ones(5,5) / 25;I1 = imfilter(I,h1);I2 = imfilter(I,h2);figure(1), imshow(I), title(Original Image);figure(2), imshow(I1), title(Filtered Image With 3*3 )figure(3), imshow(I2), title(Filtered Image With 5*5 )% 加入Gaussian 噪声J1 = imnoise(I,gaussian,0,0.005);% 加入椒盐噪声J2 = imnoise(I,salt & pepper,0.02);% 对J1、J2进行平均值平滑滤波K1 = imfilter(J1,fspecial(average,3);K2 = imfilter(J2,fspecial(average,3);figure(4);subplot(2,2,1), imshow(J1) , title(gaussian);subplot(2,2,2), imshow(J2), title(salt & pepper );subplot(2,2,3), imshow(K1), title(average );subplot(2,2,4), imshow(K2);% 对J1、J2进行中值滤波K3 = medfilt2(J1,3 3);K4 = medfilt2(J2,3 3);figure(5);subplot(2,2,1), imshow(J1) , title(gaussian);subplot(2,2,2), imshow(J2), title(salt & pepper );subplot(2,2,3), imshow(K3), title( Median filtering );subplot(2,2,4), imshow(K4)4空域锐化滤波clear all; close allI = imread(moon.tif);w=fspecial(laplacian,0)w8=1,1,1;1,-8,1;1,1,1I1= imfilter(I,w, replicate);figure(1); imshow(I), title(Original Image);figure(2), imshow(I1), title(Laplacian Image);f = im2double(I);f1= imfilter(f,w, replicate);figure(3), imshow(f1,), title(Laplacian Image);f2= imfilter(f,w8, replicate);f4 = f-f1;f8 = f-f2;figure(4), imshow(f4);figure(5), imshow(f8);。1傅立叶变换(1) 简单人工二值图像clear all;close all;clc;f = zeros(50,50);f(15:35,23:28) = 1;figure(1), imshow(f,notruesize)F = fft2(f,128,128);F1 = fftshift(F);figure(2), imshow(log(abs(F1), -1 5); colormap(gray); colorbarfigure(3), mesh(1:128,1:128, abs(F1); colormap(gray); colorbarF2 = fft2(imrotate(f,90),128,128);F3 = fftshift(F2);figure(4), imshow(imrotate(f,90),notruesize)figure(5), imshow(log(abs(F3), -1 5); colormap(gray); colorbarfigure(6), mesh(1:128,1:128, abs(F3); colormap(gray); colorbar（2）Laplacian 图像锐化增强的频域实现clear all;close all;clc;I = imread(moon.tif);I = im2double(I); %标定到0-1imshow(I);MN=size(I);PQ=2*MN;F=fft2(I,PQ(1),PQ(2);F1 =abs(F);figure,imshow(F1,);F2=log(1+abs(F);figure,imshow(F2,);F3=log(1+abs(fftshift(F);figure,imshow(F3,);h = fspecial(laplacian);H = fft2 (h,PQ(1),PQ(2);H = fftshift(H);H1=log(1+abs(H);figure,imshow(H1,);% calculate the laplacian imageG = fftshift(F).*H;g = real(ifft2(ifftshift(G);g1 = imcrop(g,1,1,MN(2)-1,MN(1)-1); figure, imshow(mat2gray(g1),);% calculate the enhanced image using laplacian operatorg2 = g1/max(max(abs(g1); %标定到0-1G = I-g2;figure, imshow(G); (3) 频率域平滑滤波close all;clear all;clc;I = imread(text.png);imshow(I);MN=size(I);PQ=2*MN;F = fft2(I,PQ(1),PQ(2);h = fspecial(average);H = fft2 (h,PQ(1),PQ(2);I1 = real(ifft2(F .* H);G = imcrop(I1, 1,1,MN(2)-1, MN(1)-1);figure, imshow(G,); %Display, scaling data to appropriate range.2离散余弦变换（DCT）% 对cameraman.tif图像的离散余弦变换及逆变换重建clear all, close allf=imread(cameraman.tif); %读一幅cameraman.tifimshow(f);F=dct2(f); %做余弦变换AbsFT=abs(F);figure, imshow(log(AbsFT);FinvT=idct2(F); %做余弦反变换figure, imshow(mat2gray(FinvT);%仅保留余弦变换频谱的左上角50*50个数据，然后做余弦反变换，观察输出图像F1=F;m,n=size(F1);F1(50:m,50:n)=0;AbsFT=abs(F1);figure, imshow(log(AbsFT);FinvT=idct2(F1); %做余弦反变换figure, imshow(mat2gray(FinvT);1膨胀与腐蚀(Dilation and Erosion)(1)对简单二值图像进行膨胀与腐蚀clear all, close allBW = zeros(9,10); BW(4:6,4:7) = 1;BWSE = strel(square,3) BW1 = imdilate(BW,SE)BW2 = imerode (BW,SE)figure(1),subplot(1,2,1), imshow(BW,notruesize), title( Original Image );subplot(1,2,2), imshow(BW1,notruesize), title( Dilated Image );figure(2),subplot(1,2,1), imshow(BW,notruesize), title( Original Image );subplot(1,2,2), imshow(BW2,notruesize), title( Eroded Image );(2)对文本图像进行膨胀与腐蚀clear all, close allI = imread(Fig0907(a)(text_gaps_1_and_2_pixels).tif); % 说明：上述图片可从Gonzalez网站下载SE = 0,1,0;1,1,1;0,1,0BW1 = imdilate(I, SE);BW2 = imerode (I, SE);figure(1),subplot(1,2,1), imshow(I,notruesize), title( Original Image );subplot(1,2,2), imshow(BW1,notruesize), title( Dilated Image );figure(2),subplot(1,2,1), imshow(I,notruesize), title( Original Image );subplot(1,2,2), imshow(BW2,notruesize) , title( Eroded Image );2. 开、闭操作（Opening and Closing）clear all, close all;I = imread(Fig0911(a)(noisy_fingerprint).tif); % 说明：上述图片可从Gonzalez网站下载se = strel(square,3);bw1 = imopen(I,se);subplot(1,2,1), imshow(I), title(Original Image)subplot(1,2,2), imshow(bw1), title(After opening)bw2 = imclose(I,se);figure;subplot(1,2,1), imshow(I, truesize), title( Original Image)subplot(1,2,2), imshow(bw2, truesize), title(After Closing)说明：改变结构元素形状、大小，重做上述实验，比较实验结果，分析结构元素对运算的影响；3细化与骨架抽取clear all, close allBW = imread(logo.tif);BW1 = bwmorph(BW,thin,Inf);BW2 = bwmorph(BW,skel,Inf);subplot(1,3,1), imshow(BW), title( Original Image );subplot(1,3,2), imshow(BW1), title( Thinned Image );subplot(1,3,3), imshow(BW2), title( Image skeleton);%查看bwmorph函数使用说明help bwmorph1图像阈值分割clear all, close all;I = imread(rice.tif);figure (1),imshow(I)figure(2); imhist(I)T=120/255;Ibw1 = im2bw(I,T); %选择阈值T=120/255对图像二值化；figure(3);subplot(1,2,1), imshow(Ibw1);T=graythresh(I); %采用Otsu方法计算最优阈值T对图像二值化；L = uint8(T*255)Ibw2 = im2bw(I,T); subplot(1,2,2), imshow(Ibw2);help im2bw;help graythresh;（令取不同值，重做上述试验，观察试验结果）2边缘检测clear all, close all;I = imread(rice.tif);BW1 = edge(I,sobel);BW2 = edge(I,canny);BW3 = edge(I,prewitt);BW4 = edge(I,roberts);BW5 = edge(I,log);figure(1), imshow(I), title(Original Image);figure(2), imshow(BW1), title(sobel);figure(3), imshow(BW2), title(canny);figure(4), imshow(BW3), title(prewitt);figure(5), imshow(BW4), title(roberts);figure(6), imshow(BW5), title(log);（二）图像分割中运动的运用（运动目标检测）% Moving object detection ver1.0 2012-10-23% Writen by Zhangyunchuclear all;close all;clc;% read a avi file information, cartest.avi 为待处理的视频文件，格式为.avifileinfo = aviinfo(cartest.avi); numFrames = fileinfo.NumFrames;% To avoid consuming too much memories, read only a clip of 200 frames each timeclipframes = 200;Clips = floor(numFrames/clipframes);% Read the first frame in the video sequence as the reference background image %在matlab2010 版本中，可采用函数mmreader，详见matlab2010 helpFref = aviread(cartest.avi,10);Iref =Fref.cdata; % predefine the thresholdThreh = 15;% Define a structure elementse=strel(disk,3);for m = 1:Clips % Calculate the start and end frame index of the m-th movie clip startframe = (m - 1)* clipframes + 1; if m = Clips endframe = numFrames -1; else