MATLAB数字图像处理 - Benjamin_Han
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\');
posted on 2018-04-02 21:36 Benjamin_Han 阅读(1964) 评论(0) 编辑收藏举报
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