实验二图像直方图分析

1、姓名：马晓正 学号：201215502实验二 图像直方图分析一实验目的及要求1了解MATLAB的操作环境和基本功能。2掌握MATLAB中图像增强与平滑的函数的使用方法。3加深理解图像增强与平滑的算法原理。二、实验内容（一）研究以下程序，分析程序功能；输入执行各命令行，认真观察命令执行的结果。熟悉程序中所使用函数的调用方法，改变有关参数，观察试验结果。（可将每段程序保存为一个.m文件）1直方图均衡化clear all; close all % Clear the MATLAB workspace of any variables % and close open figure windows.I

2、 = 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 equalize

3、d 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

4、 newly written file注意：imadjust()功能：调整图像灰度值或颜色映像表，也可实现伽马校正。语法：J = imadjust(I,low_in high_in,low_out high_out,gamma)newmap = imadjust(map,low_in high_in,low_out high_out,gamma)RGB2 = imadjust(RGB1,.)2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, imsho

5、w(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

6、) / 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 & pepp

7、er,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 = medfi

8、lt2(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

9、,-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);figur

10、e(5), imshow(f8);（二）采用MATLAB底层函数编程实现1灰度变换之动态范围扩展假定原图像f(x, y)的灰度范围为a, b，希望变换后图像 g(x, y)的灰度范围扩展至c, d，则线性变换可表示为： 用MATLAB底层函数编程实现上述变换函数。观察图像 pout.tif的灰度直方图，选择合适的参数a, b、c, d对图像pout.tif进行灰度变换，以获得满意的视觉效果。2非锐化掩蔽和高斯滤波从原图像中减去其非锐化（平滑过的）图像的过程称为非锐化掩蔽，其基本步骤为：对原图像进行平滑滤波得到模糊图像；从原图像中减去模糊图像，产生的差值图像称为模板；将模板加到原图像上，得到锐化

11、后的图像。 即，用MATLAB函数编程实现上述功能。三、实验设备1计算机；2MATLAB6.5及以上；四、预习与思考1预习实验内容，阅读教材熟悉实验原理；2查阅资料，熟悉MATLAB的操作环境和基本功能。熟悉实验中涉及的有关函数。3利用课余时间，用MATLAB底层函数编程实现实验内容（二）中的灰度线性变换。4你能否给出实现样例程序功能的其它方法？五、实验报告要求1. 简述试验的目的和试验原理；2. 叙述各段程序功能，改变有关函数的参数，分析比较实验结果；3. 打印出所编写的实验程序。4. 写出本实验的心得体会及意见。1直方图均衡化I = imread(pout.tif); imshow(I)

12、figure, imhist(I) I2,T = histeq(I); figure, imshow(I2) figure, imhist(I2) figure,plot(0:255)/255,T); imwrite (I2, pout2.png); imfinfo(pout2.png) 注意：imadjust()功能：调整图像灰度值或颜色映像表，也可实现伽马校正。语法：J = imadjust(I,low_in high_in,low_out high_out,gamma)newmap = imadjust(map,low_in high_in,low_out high_out,gamma)

13、RGB2 = imadjust(RGB1,.)2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)hel

14、p imadjust 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 )%

15、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),

16、 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, repli