PROJECT 03-01 ： Image Enhancement Using Intensity Transformations
实验要求：
Objective
To manipulate a technique of image enhancement by intensity transformation or gray level transformation.
Main requirements:
Ability of programming with C, C++, or Matlab.
Instruction manual:
The focus of this project is to experiment with intensity transformations to enhance an image. Download Fig. 3.8(a) and enhance it using
(a) The log transformation of Eq. (3.2-2).
(b) A power-law transformation of the form shown in Eq. (3.2-3).
In (a) the only free parameter is c, but in (b) there are two parameters, c and r for which values have to be selected. As in most enhancement tasks, experimentation is a must. The objective of this project is to obtain the best visual enhancement possible with the methods in (a) and (b). Once (according to your judgment) you have the best visual result for each transformation, explain the reasons for the major differences between them.
实验代码：
clear all
clc
close all
% read image
img_gray = imread('Fig4.28(a).jpg');
% img_gray = rgb2gray(img);
figure(1)
subplot(1, 2, 1);
imshow(img_gray);
title('original');
% log transformation
t1 = log(1+double(img_gray));
% Description of mat2gray:
% I = mat2gray(A, [amin amax]) converts the matrix A to the intensity image
% I. The returned matrix I contains values in the range 0.0 (black) to 1.0
% (full intensity or white). amin and amax are the values in A that correspond
% to 0.0 and 1.0 in I. Values less than amin become 0.0, and values greater
% than amax become 1.0.
t2 = mat2gray(t1);
% Description of im2uint8:
% im2uint8 takes an image as input and returns an image of class uint8.
% If the input image is of class uint8, the output image is identical to
% the input image. If the input image is not uint8, im2uint8 returns the
% equivalent image of class uint8, rescaling or offsetting the data as necessary.
img1 = im2uint8(t2);
subplot(1, 2, 2);
imshow(img1);
title('log transformation');
% power-law transformation
figure(2)
% subplot(2, 5, 1);
imshow(img_gray);
title('original');
img_t1 = double(img_gray);
cnt = 1;
pow = 0.1;
for pow = 0.1:0.2:0.9
img_t2 = im2uint8(mat2gray(img_t1.^pow));
cnt = cnt + 1;
figure(1+cnt);
% subplot(2, 5, cnt);
imshow(img_t2);
% title('power-law:gamma=');
gamma = sprintf('power-law:gamma=%.1f', pow);
title(gamma);
end
运行结果：
首先是原图像与做对数变换后的结果对比，随后是幂率变换的结果。程序中已有详细注释。
PROJECT 03-02 [Multiple Uses] : Histogram Equalization
实验要求：
Objective
To manipulate a technique of image enhancement by histogram equalization.
Main requirements:
Ability of programming with C, C++, or Matlab.
Instruction manual:
(a) Write a computer program for computing the histogram of an image.
(b) Implement the histogram equalization technique discussed in Section 3.3.1.
(c) Download Fig. 3.8(a) and perform histogram equalization on it.
As a minimum, your report should include the original image, a plot of its histogram, a plot of the histogram-equalization transformation function, the enhanced image, and a plot of its histogram. Use this information to explain why the resulting image was enhanced as it was.
简单点来说，实验中我们要进行直方图均衡化，可以调用MATLAB工具箱中的histeq函数。
上代码：
clear all;
clc;
close all;
%%
img = imread('Boat512.bmp');
subplot(2,2,1);
imshow(img);
subplot(2,2,2);
imhist(img);
img1 = histeq(img,256);
subplot(2,2,3);
imshow(img1);
subplot(2,2,4);
imhist(img1);运行结果：
进行直方图均衡化之后，我们可以很明显地看到图像的对比度增强了。
PROJECT 03-03 [Multiple Uses] ：Arithmetic Operations
实验要求：
Objective
To know how to do arithmetic operations on an image and the functions of some arithmetic operations.
Main requirements:
Ability of programming with C, C++, or Matlab.
Instruction manual:
Write a computer program capable of performing the four arithmetic operations between two images. This project is generic, in the sense that it will be used in other projects to follow. (See comments on pages 112 and 116 regarding scaling). In addition to multiplying two images, your multiplication function must be able to handle multiplication of an image by a constant.
实验中我们要对图像做算术运算，观察结果。
实验代码：
%
clear all;
clc;
close all;
%
% img = imread('peppers_color.jpg');
% size = size(img);
% if numel(size) >= 2
% img = rgb2gray(img);
% imwrite(img,'gray_img.jpg');
% end
% clear size;
% 读取原图像
img = imread('gray_img.jpg');
subplot(4,3,1);
imshow(img);
title('image 1');
% 反转灰度
img1 = 255 - img;
subplot(4,3,2);
imshow(img1);
title('image 2');
% 图像相加
img2 = imadd(img, img1);
subplot(4,3,3);
imshow(img2);
title('add');
% 图像相减
subplot(4,3,4);
imshow(img2);
title('image 1');
subplot(4,3,5);
imshow(img);
title('image 2');
img3 = imsubtract(img2, img);
subplot(4,3,6);
imshow(img3);
title('subtract');
% 图像相乘
subplot(4,3,7);
imshow(img);
title('image 1');
subplot(4,3,8);
imshow(img1);
title('image 2');
img4 = immultiply(img, img1);
subplot(4,3,9);
imshow(img4);
title('multiply');
%% 图像相除
subplot(4,3,10);
imshow(img4);
title('image 1');
subplot(4,3,11);
imshow(img1);
title('image 2');
img5 = imdivide(img4, img1);
subplot(4,3,12);
imshow(img5);
title('divide');实验结果：
PROJECT 03-04 [Multiple Uses] ： Spatial Filtering
实验要求：
Objective
To understand what is special filtering and how the parameters of the filtering mask affect the output of filters..
Main requirements:
Ability of programming with C, C++, or Matlab.
Instruction manual:
Write program to perform spatial filtering of an image (see Section 3.5 regarding implementation). You can fix the size of the spatial mask at 3 x 3, but the coefficients need to be variables that can be input into your program. This project is generic, in the sense that it will be used in other projects to follow.
使用3 x 3的滤波器模板，对图像进行空间滤波。程序中调用MATLAB的fspecial函数生成3 x 3滤波器模板。
%
close all;
clc;
clear all;
%
img = imread('Fig5.10(a).jpg');
subplot(1,3,1);
imshow(img);
title('original');
% 均值滤波
% h = fspecial('average', hsize) returns an averaging filter h of size hsize.
% The argument hsize can be a vector specifying the number of rows and columns
% in h, or it can be a scalar, in which case h is a square matrix. The default
% value for hsize is [3 3].
h = fspecial('average',[3,3]); % 均值滤波器
img1 = imfilter(img, h);
subplot(1,3,2);
imshow(img1);
title('average filter');
% 中值滤波
% B = medfilt2(A) performs median filtering of the matrix A using the default
% 3-by-3 neighborhood.
img2 = medfilt2(img);
subplot(1,3,3);
imshow(img2);
title('median filter');
实验结果：
PROJECT 03-05 ： Enhancement Using the Laplacian
实验要求：
Objective:
To further understand the well-known technique of Laplacian and how it works on an image.
Main requirements:
Ability of programming with C, C++, or Matlab.
Instruction manual:
(a) Use the programs developed in Projects 03-03 and 03-04 to implement the Laplacian enhancement technique described in connection with Eq. (3.7-5). Use the mask shown in Fig. 3.39(d).
(b) Duplicate the results in Fig. 3.40. You will need to download Fig. 3.40(a).
使用拉普拉斯算子对图片进行空间滤波。
实验代码：
%
close all;
clc;
clear all;
%
img = imread('moon.jpg');
subplot(3, 1, 1);
imshow(img);
title('original');
%
h = fspecial('laplacian', 0.2);
img1 = imfilter(img, h);
subplot(3, 2, 3);
imshow(img1);
title('default laplacian');
%
w = [-1, -1, -1; -1, 8, -1; -1, -1, -1];
% 'replicate', 图像大小通过复制外边界的值来扩展
img2 = imfilter(img, w, 'replicate');
subplot(3, 2, 4);
imshow(img2);
title('mask');
%
img3 = img + img1;
subplot(3, 2, 5);
imshow(img3);
title('output1');
%
img4 = img + img2;
subplot(3, 2, 6);
imshow(img4);
title('output2');
实验结果：
可以看出使用拉普拉斯算子可以突出边缘，将其与原图像叠加便能增强边缘。左边的是使用fspecial生成拉普拉斯算子，右边的是直接输入的拉普拉斯算子模板，即：
w = [-1, -1, -1; -1, 8, -1; -1, -1, -1];PROJECT 03-06 ：Unsharp Masking
实验要求：
Objective:
To further understand image enhancement technique of unsharp masking and how it works on an image.
Main requirements:
Ability of programming with C, C++, or Matlab.
Instruction manual:
(a) Use the programs developed in Projects 03-03 and 03-04 to implement high-boost filtering, as given in Eq. (3.7-8). The averaging part of the process should be done using the mask in Fig. 3.34(a).
(b) Download Fig. 3.43(a) and enhance it using the program you developed in (a). Your objective is to choose constant A so that your result visually approximates Fig. 3.43(d).
非锐化掩蔽，使用前面实验的程序来实现增强滤波。
直接上程序：
%
close all;
clc;
clear all;
%
img = imread('test.png');
figure(1);
subplot(2,2,1);
imshow(img);
title('original');
cnt = 1;
for alpha = [0.1 0.4 0.9]
h = fspecial('laplacian', alpha);
img_temp =imfilter(img, h);
img_out = img + img_temp;
cnt = cnt + 1;
subplot(2,2,cnt);
imshow(img_out);
title(['alpha = ', num2str(alpha)]);
% subplot(1, 2, 1);
% imshow(img_temp);
% title(['alpha = ', num2str(alpha)]);
% subplot(1, 2, 2);
% imshow(img_out);
% title('output');
end实验结果：
