数字图像处理实验（5）：Proj03-01 ~ Proj03-06 标签： 图像处理matlab 2017-04-30 10:39 184人阅读

实验要求：

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。

实验要求：

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：

实验要求：

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：

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：

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：