实验一--图像增强与平滑.doc

实验一 图像增强与平滑
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。
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
% an array named I
imshow(I) . %display the image
figure, imhist(I) % Create a histogram of the image and display it in
% a new figure window.
[I2,T] = histeq(I); % Histogram equalization.
%[J,T] = histeq(I,...) returns the grayscale transformation that maps gray levels in the %intensity image I to gray levels in J.
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 file

均衡化后的图象对比度变强。
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 all
I = 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 all
I = 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 all
I = imread('moon.tif');
w=fspecial('laplacian',0.2)
w8=[1,1,1;1,-8,1;1,1,1]
I1= 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);
w =0.1667 0.6667 0.1667
0.6667 -3.3333 0.6667
0.1667 0.6667 0.1667
w8 = 1 1 1
1 -8 1
1 1 1

5．图像的伪彩色处理—密度分割
clear all, close all
I = imread('ngc4024m.tif');
X = grayslice(I,16);
imshow(I), title('Original Image')
figure, imshow(X,jet(16)), title('Index Color Image')

（二）采用MATLAB底层函数编程实现以下灰度线性变换
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。