MATLAB数字图像处理

工具： MATLAB；

动机：使用 MATLAB toolbox 程序设计相对简单，通过初步学习一些图像处理的常见技术，帮助建立这方面的技能体系；

面向对象：有过经验，但是一头雾水的我。

1 图像、视频文件读取

clear all; close all; clc;

% Read an image
A = imread('lena.jpg');

% Display the read image
figure, imshow(A);

% Print height, width and number of channels of the read image
height = size(A, 1);
width = size(A, 2);
number_of_channels = size(A, 3);

% Resize the image to 2x and display
B = imresize(A, 2.0);
figure, imshow(B);

% Rotate the image 逆时针旋转
C = imrotate(A, 90);
figure, imshow(C);

　关键点：imread,figure(imshow),size获取高度，宽度，图像通道数的参数，图像缩放，旋转。

clear all; close all; clc;

% Read an image
A = imread('lena.jpg');

% Translate the image
A_trans = imtranslate(A, [5 15]);

% Write the transformed image to disk
imwrite(A_trans, 'newlena.jpg');

figure,imshow(A_trans);

　关键：imwrite及其调用前做的工作。

1-1 视频读取（选看）

clear all; close all; clc;

% Initialize an object for reading video

videoObj = vision.VideoFileReader('video1.mp4', 'ImageColorSpace', 'RGB');

% Get video frame size and frame rate

S = info(videoObj);
width = S.VideoSize(1);
height = S.VideoSize(end);
frame_rate = S.VideoFrameRate;

% Get individual video frames
image_data = step(videoObj);

% Run the loop until all frames have been displayed
while ~isDone(videoObj)
    % Display video frame one by one
    imshow(image_data);
    
    image_data = step(videoObj);
end

% Release the object for reading video
release(videoObj);

　关键：step，其流程reader读取生成对象，得到信息，逐帧显示 , PS:视频教程中有另外一种实现。 

3 图像格式

 3.1 RGB、灰度图片

clear all; close all; clc;

% Read a RGB image
A = imread('lena.jpg');

% Verify number of channels
number_of_channels = size(A, 3)

% Convert RGB image to Grayscale
A_gray = rgb2gray(A);
number_of_channels2 = size(A_gray, 3)

figure, subplot(1, 2, 1), imshow(A), title('Input RGB image');
subplot(1, 2, 2), imshow(A_gray), title('Converted Grayscale image');

　

关键：灰度图像颜色通道位1，rgb图像颜色通道数是3，subplot 显示多个图像，便于查看。

clear all; close all; clc;

% Read a RGB image
A = imread('lena.jpg');

% Extract individual R, G & B channels from RGB image
R = A(:, :, 1);
G = A(:, :, 2);
B = A(:, :, 3);

% Convert RGB image to HSV color space
A_hsv = rgb2hsv(A);

% Extract individual H, S & V channels from HSV image
H = A_hsv(:, :, 1);
S = A_hsv(:, :, 2);
V = A_hsv(:, :, 3);

% Display the read RGB image and HSV converted image
figure, subplot(1, 2, 1), imshow(A), title('Read RGB image');
subplot(1, 2, 2), imshow(A_hsv), title('Converted HSV image');

% Displaying individual image channels.
figure, subplot(1, 3, 1), imshow(S), title('Saturation channel');
subplot(1, 3, 2), imshow(H), title('Hue channel');
subplot(1, 3, 3), imshow(V), title('Value channel');

　　

关键：R、G、B、H、S、V 都是提取空间域（矩阵表示）的信息，位图片大小，RGB、HSV格式都是3个颜色通道，前者unit8，后者double格式。PS：HSV 有用的是V（当前）。

4 直方图及其均衡化

 直方图以前都是空间域上对像素操作，直方图则是借助频率来获取图像的统计信息 imhist。

clear all; close all; clc;

% Read an image
A = imread('lena.jpg');
B = imread('lena.jpg');

% Convert the read image to single-channel
A = rgb2gray(A);

% Plot and display the histogram if A is a single channel image with
% default number of bins
figure, subplot(1,3,1),imhist(A), title('Histogram of read image');
        subplot(1,3,2),imshow(A),title('Grayscale image');
        subplot(1,3,3),imshow(B),title('RGB image');

　　

直方图：左边暗，右边亮，不同亮度出现的频率，注意直方图应该表示一个颜色通道的统计信息，不要把RGB、HSV等格式图片多个通道的格式混合起来，这样是得不到可分析的信息的，RGB如下直方图生成方式如下：

clear all; close all; clc;

% Read an image
A = imread('lena.jpg');

% Plot and display the histogram if A is a 3 channel image with
% default number of bins
figure, imhist(A(:, :, 1)), title('Histogram of 1st channel of read image');
figure, imhist(A(:, :, 2)), title('Histogram of 2nd channel of read image');
figure, imhist(A(:, :, 3)), title('Histogram of 3rd channel of read image');

　　

 5 直方图均衡化

亮度分布集中在左边图片太暗，集中在右边图片太亮，直方图均衡化就是让图像的不同像素点亮度均匀话，对灰度图片和其他格式图片有不同的做法

clear all; close all; clc;

% Read an image
A = imread('test1.jpg');

% Convert the image to single channel image
A = rgb2gray(A);

% Equalize the histogram of read single channel image
A_histeq = histeq(A);

% Display original and equalized images side by side with their respective
% histogram plots
figure, subplot(2, 2, 1), imshow(A), title('Original Image');
subplot(2, 2, 2), imshow(A_histeq), title('Equalized Image');
subplot(2, 2, 3), imhist(A), title('Histogram of Original Image');
subplot(2, 2, 4), imhist(A_histeq), title('Histogram of Equalized Image');

　　

关键：imhist,histeq,

clear all; close all; clc;

% Read a RGB image
A = imread('test1.jpg');

% Convert the RGB image into HSV image
A_hsv = rgb2hsv(A);

% Perform histogram equalization of V-channel in HSV image which is the 3rd
% channel
A_hsv(:, :, 3) = histeq(A_hsv(:, :, 3));

% Convert back equalized HSV image into RGB image
A_histeq = hsv2rgb(A_hsv);

% Display original and equalized RGB images side by side
figure, subplot(1, 2, 1), imshow(A), title('Original RGB image');
subplot(1, 2, 2), imshow(A_histeq), title('Equalized RGB image');

　　

关键：histeq(A(:,:,3) ,A是hsv格式，流程：RGB图片转化为HSV 图片，然后对HSV图片的V进行均衡化，均衡化后重新转化为RGB图片。

 6 图像平滑化（image smoothing）

释义：消除、弱化、压缩图像的噪声、边缘、细节、突变。可以在空间域（滤波）和频域上进行。fspecial、infilter

空间域： 生成一个任何大小的矩阵，用该矩阵对图像某个像素及其周围像素进行集体操作后代替原像素。根据将像素值设置为不同的值类型，可以分为：最小化，最大化，均值，加权均值操作。

空间滤波示例，流程：生成灰度图像，生成滤波器，应用滤波，执行滤波操作，PS：中值滤波过程不一样。

clear all; close all; clc;

% Read an input image
A = imread('test1.jpg');

% Conversion to single channel image
A = rgb2gray(A);

% Generate Average filter
average_h = fspecial('average', [9 9]);

% Generate Gaussian filter
gaussian_h = fspecial('gaussian', [13 13]);

% Filter input image using average filter
B_average = imfilter(A, average_h);

% Filter input image using Gaussian filter
B_gaussian = imfilter(A, gaussian_h);

% Filter input image using median filter
B_median = medfilt2(A, [5 5]);

% Display input and filtered images side by side
figure, subplot(2, 2, 1), imshow(A), title('Original input image');
subplot(2, 2, 2), imshow(B_average), title('Image filtered using Averaging filter');
subplot(2, 2, 3), imshow(B_gaussian), title('Image filtered using Gaussian filter');
subplot(2, 2, 4), imshow(B_median), title('Image filtered using Median filter');

　　

均值滤波是最简单的空间滤波，特点：1、非加权；2、适用于去噪声，但会使图片模糊；3、突出总体细节

6.2 中值滤波（加权平滑滤波器）

pixel 有不同权值的滤波器，特点：越靠近中心的像素越重要；与非权值的滤波操作（均值滤波）相比，更好的模糊化，滤波器构造如下图所示。

补充一个导向滤波的例子，这么做的目的是保留图像的边缘。

clear all; close all; clc;

% Read an image
A = imread('test1.jpg');

% Filter the image using guided filter
A_guided_filtered = imguidedfilter(A);

% Display original and processed image side-by-side
imshowpair(A, A_guided_filtered, 'montage');

　　

7 边缘检测

边缘是重要的图像强度局部变化像素点的集合。边缘方向：垂直于图像强度最大强度变化的方向。

思路：空间域上对 x, y 求导，大于特定的值就是边缘，不同的算子生成方式有不同的近似求导方案。

clear all; close all; clc;

% Read an image
A = imread('test1.jpg');

% Convert the image to single-channel intensity image if the read image
% is RGB (3-channel)
A_gray = rgb2gray(A);

% Method for Sobel edge detection
BW_sobel = edge(A_gray, 'sobel');

% Method for Prewitt edge detection
BW_prewitt = edge(A_gray, 'prewitt');

% Method of Canny edge detection
BW_canny = edge(A_gray, 'canny');

% Display the images together
figure, subplot(2, 2, 1), imshow(A_gray), title('Original');
subplot(2, 2, 2), imshow(BW_sobel), title('Sobel');
subplot(2, 2, 3), imshow(BW_prewitt), title('Prewitt');
subplot(2, 2, 4), imshow(BW_canny), title('Canny');

8 二值图像(im2bw)

图像只有两个值0,1,根据直方图左边暗，右边亮，左边取0（黑），右边取1（白），二值图像是根据直方图，大于某个值则设置为1（白色），二值图像生成的关键就是这个阈值的选择，否则二值图像生成的背景、人物、物体会杂糅在一起，没有办法继续处理。

流程：灰度 - double - im2bw，第2个参数要 ∈[0,1] ，阈值除以 255 带入 。 PS:  otsu 算法已经集成在matlab 工具箱中了

clear all;close all;clc;

% Read an input image
A = imread('test1.jpg');

% Convert the image to single-channel grayscale image
A_gray = rgb2gray(A);

figure,imhist(A_gray),title('hist of A_grey');

% Convert image to double i.e., [0,1]
A_gray = im2double(A_gray);

% Generate threhold value using Otsu's algorithm
otsu_level = graythresh(A_gray);

% Threshold image using Otsu's threshold and manually defined
% threshold values
B_otsu_thresh = im2bw(A_gray, otsu_level);
B_thresh_50 = im2bw(A_gray, 50/255);
B_thresh_100 = im2bw(A_gray, 100/255);
B_thresh_150 = im2bw(A_gray, 150/255);
B_thresh_200 = im2bw(A_gray, 200/255);

% Display original and thresholded binary images side-by-side
figure, subplot(2, 3, 1), imshow(A_gray), title('Original image');
subplot(2, 3, 2), imshow(B_otsu_thresh), title('Binary image using Otsu threshold value');
subplot(2, 3, 3), imshow(B_thresh_50), title('Binary image using threshold value = 50');
subplot(2, 3, 4), imshow(B_thresh_100), title('Binary image using threshold value = 100');
subplot(2, 3, 5), imshow(B_thresh_150), title('Binary image using threshold value = 150');
subplot(2, 3, 6), imshow(B_thresh_200), title('Binary image using threshold value = 200');

　

9 图像降噪 （ fsepcial,imfilter）

噪声来源于 图像采集及转化、传输过程。

噪声分类： 高斯：正态分布；椒盐：一定概率分布的黑白像素点；冲击噪声：随机白点。

降噪手段：最简单的是均值滤波，但是会模糊整个图片，中值滤波：适用于椒盐噪声。

周期性的噪声多来自于电子电磁干扰，噪声在图像中的分布具有规律性（周期性），傅里叶域的频域技术咋消除周期性噪声是最有效的。带阻滤波：适合移除图片噪声及移除特定范围的频率。

clear all; close all; clc;

% Read an input image
A = imread('test1.jpg');

% Generate Gaussian and Average filters
h_gaussian = fspecial('gaussian');
h_average = fspecial('average');

% Filter input image using generated Gaussian and Average filters
B_gaussian = imfilter(A, h_gaussian);
B_average = imfilter(A, h_average);

% Filter input image using Median filter
% Median filtering function takes as input grayscale image
A_gray = rgb2gray(A);
B_median = medfilt2(A_gray);

% Display original and filtered images side-by-side for comparing the
% result of image de-noising
figure, subplot(2, 2, 1), imshow(A), title('Original image with Salt & Pepper noise');
subplot(2, 2, 2), imshow(B_gaussian), title('Input image filtered using Gaussian filter');
subplot(2, 2, 3), imshow(B_average), title('Input image filtered using Average filter');
subplot(2, 2, 4), imshow(B_median), title('Input image filtered using Median filter');

　　

 10 形态学

Erosion 腐蚀 ：imerode 分离相连物体，去毛刺，让物体变小；Dilation 膨胀 imdilate，拼接物体，填表面小坑，物体变大。

close all; clear all; clc;

% Read an input image
A = imread('binaryImg1.jpg');

% Convert to single channel
A = rgb2gray(A);

% Generate structuring element for use
se = strel('disk', 15); 

% Perform image erosion
B_eroded = imerode(A, se);

% Perform image dilation
B_dilated = imdilate(A, se);

% Display the images side-by-side for comparison
figure, subplot(1, 3, 1), imshow(A), title('Original read image');
subplot(1, 3, 2), imshow(B_eroded), title('Original image after Erosion');
subplot(1, 3, 3), imshow(B_dilated), title('Original image after Dilation');

开运算：先腐蚀再膨胀，分离物体、去毛刺、去小点（物体点），去小部件，表面坑变小。

闭运算：先膨胀再腐蚀，表面坑被填，内部背景点消失，表面坑被填，毛刺不变。

clear all; close all; clc;

% Read an input image
A = imread('binaryImg1.jpg');

% Convert read image to single channel image
A = rgb2gray(A);

% Generate structuring element for processing
se = strel('disk', 15); 

% Perform image opening operation
B_open = imopen(A, se);

% Perform image closing operation
B_close = imclose(A, se);

% Display original and processed images
figure, subplot(1, 3, 1), imshow(A), title('Original image');
subplot(1, 3, 2), imshow(B_open), title('Original image after Opening');
subplot(1, 3, 3), imshow(B_close), title('Original image after Closing');

　　

    

 利用膨胀得到物体边缘

clear all; close all; clc;

% Read an input image
A = imread('binaryImg1.jpg');

% Convert the read image to single channel image
A = rgb2gray(A);

% Generate structuring element for use
se = strel('disk', 5); 

% Perform image dilation
% To get the object boundary, subtract the original image from the dilated
% version of the orginal image
B_dilated = imdilate(A, se);

% Subtract the original image from dilated image
B_boundary = B_dilated - A;

% Display images side by side
figure, subplot(1, 3, 1), imshow(A), title('Original image');
subplot(1, 3, 2), imshow(B_dilated), title('Original image after Dilation');
subplot(1, 3, 3), imshow(B_boundary), title('Original image with highlighted binary object boundaries');

　　

利用腐蚀得到物体边缘

clear all; close all; clc;

% Read an input image
A = imread('binaryImg1.jpg');

% Convert the read image to single channel image
A = rgb2gray(A);

% Generate structuring element for use
se = strel('disk', 5); 

% Perform image erosion
B_eroded = imerode(A, se);

% Subtract the eroded image from original image
B_boundary = A - B_eroded;

% Display images side by side
figure, subplot(1, 3, 1), imshow(A), title('Original image');
subplot(1, 3, 2), imshow(B_eroded), title('Original image after Erosion');
subplot(1, 3, 3), imshow(B_boundary), title('Original image with highlighted binary object boundaries');