数字图像处理,冈萨雷斯,课件英文版07小波变换与多分辨率处理

数字图像处理课程设计-小波变换

摘要 小波变换的理论是近年来兴起的新的数学分支，素有“数学显微镜”的美称。它是继1822年傅立叶提出傅立叶变换之后又一里程碑式的领域，解决了很多傅立叶变换不能解决的困难问题。小波变换可以使得信号的低频长时特性和高频短时特性同时得到处理，具有良好的局部化性质，能有效地克服傅氏变换在处理非平稳复杂信号时存在的局限性，具有极强的自适应性，因此在图像处理中具有极好应用价值。本设计主要分析了基于小波变换的图像分解和图像压缩技术，并运用Matlab软件对图像进行分解，然后提取其中与原图像近似的低频信息，达到对图像进行压缩的目的。分别作第一层分解和第二层分解，并比较图像压缩的效果。 关键词:小波变换；Matlab；图像分解；图像压缩

目录 摘要 ..................................................................................................... I 第1章绪论 (1) 1.1设计背景 (1) 1.2设计要求 (1) 1.3设计思路简介 (1) 第2章小波变换处理图像设计过程 (2) 2.1小波变换的分解和重构算法 (2) 2.2小波变换在图像压缩中的应用 (4) 第3章软件设计与仿真 (6) 3.1MATLAB程序 (6) 3.2结果及分析 (7) 第4章总结与展望 (9) 参考文献 (10)

第1章绪论 1.1设计背景 小波分析是当前应用数学和工程学科中一个迅速发展的新领域，经过近10年的探索研究，重要的数学形式化体系已经建立，理论基础更加扎实。与Fourier变换相比，小波变换是空间(时间)和频率的局部变换，因而能有效地从信号中提取信息。通过伸缩和平移等运算功能可对函数或信号进行多尺度的细化分析，解决了Fourier变换不能解决的许多困难问题。小波变换联系了应用数学、物理学、计算机科学、信号与信息处理、图像处理、地震勘探等多个学科。小波分析是一个新的数学分支，它是泛函分析、Fourier分析、样调分析、数值分析的完美结晶；小波分析是时间—尺度分析和多分辨分析的一种新技术，它在信号分析、语音合成、图像识别、计算机视觉、数据压缩、地震勘探、大气与海洋波分析等方面的研究都取得了有科学意义和应用价值的成果。 1.2设计要求 利用小波变换的基本原理在MATLAB环境下编写程序对静态图像进行分解并压缩，并观察分析其处理效果。 1.3设计思路简介 一个图像作小波分解后，可得到一系列不同分辨率的子图像，不同分辨率的子图像对应的频率是不相同的。高分辨率(即高频)子图像上大部分点都接近于0，越是高频这种现象越明显。对一个图像来说，表现一个图像最主要的部分是低频部分，所以利用小波分解就可以达到去掉图像的高频部分而只保留低频部分的目的。 MATLAB是矩阵实验室（Matrix Laboratory）的简称，它在数学类科技应用软件中在数值计算方面首屈一指。MATLAB可以进行矩阵运算、绘制函数和数据、实现算法、创建用户界面、连接其它编程语言的程序等，主要应用于工程计算、控制设计、信号处理与通讯、图像处理、信号检测、金融建模设计与分析等领域。 本设计利用MATLAB工具箱中的Wavele Toolbox——小波工具箱对图像进行小波变换。

数字图像处理

数字图像处理(MATLAB版) 实验指导书 (试用版) 本实验指导书配合教材和课堂笔记中的例题使用 姚天曙编写 安徽农业大学工学院 2009年4月试行

目录 实验一、数字图像获取和格式转换 2 实验二、图像亮度变换和空间滤波 6 实验三、频域处理7 实验四、图像复原9 实验五、彩色图像处理10 实验六、图像压缩11 实验七、图像分割13 教材与参考文献14

《数字图像处理》实验指导书 实验一、数字图像获取和格式转换 一、实验目的 1掌握使用扫描仪、数码相机、数码摄像级机、电脑摄像头等数字化设备以及计算机获取数字图像的方法； 2修改图像的存储格式；并比较不同压缩格式图像的数据量的大小。 二、实验原理 数字图像获取设备的主要性能指标有x、y方向的分辨率、色彩分辨率（色彩位数）、扫描幅面和接口方式等。各类设备都标明了它的光学分辨率和最大分辨率。分辨率的单位是dpi，dpi是英文Dot Per Inch的缩写，意思是每英寸的像素点数。 扫描仪扫描图像的步骤是:首先将欲扫描的原稿正面朝下铺在扫描仪的玻璃板上，原稿可以是文字稿件或者图纸照片；然后启动扫描仪驱动程序后，安装在扫描仪内部的可移动光源开始扫描原稿。为了均匀照亮稿件，扫描仪光源为长条形，并沿y方向扫过整个原稿；照射到原稿上的光线经反射后穿过一个很窄的缝隙，形成沿x方向的光带，又经过一组反光镜，由光学透镜聚焦并进入分光镜，经过棱镜和红绿蓝三色滤色镜得到的RGB三条彩色光带分别照到各自的CCD上，CCD将RGB光带转变为模拟电子信号，此信号又被A/D变换器转变为数字电子信号。至此，反映原稿图像的光信号转变为计算机能够接受的二进制数字电子信号，最后通过串行或者并行等接口送至计算机。扫描仪每扫一行就得到原稿x方向一行的图像信息，随着沿y方向的移动，在计算机内部逐步形成原稿的全图。扫描仪工作原理见图1.1。

数字图像处理英文原版及翻译

Digital Image Processing and Edge Detection Digital Image Processing Interest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception. An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pixels, and pixels. Pixel is the term most widely used to denote the elements of a digital image. Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spec- trum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra- sound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of applications. There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vi- sion, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a

计算机图形_Digital Image Processing, 2nd ed(数字图像处理(第2版))

Digital Image Processing, 2nd ed(数字图像处理(第2 版)) 数据摘要： DIGITAL IMAGE PROCESSING has been the world-wide leading textbook in its field for more than 30 years. As the 1977 and 1987 editions by Gonzalez and Wintz, and the 1992 edition by Gonzalez and Woods, the present edition was prepared with students and instructors in mind. The material is timely, highly readable, and illustrated with numerous examples of practical significance. All mainstream areas of image processing are covered, including a totally revised introduction and discussion of image fundamentals, image enhancement in the spatial and frequency domains, restoration, color image processing, wavelets, image compression, morphology, segmentation, and image description. Coverage concludes with a discussion on the fundamentals of object recognition. Although the book is completely self-contained, this companion web site provides additional support in the form of review material, answers to selected problems, laboratory project suggestions, and a score of other features. A supplementary instructor's manual is available to instructors who have adopted the book for classroom use.

数字图像处理课后题答案

1. 图像处理的主要方法分几大类 答：图字图像处理方法分为大两类：空间域处理（空域法）和变换域处理（频域法）。 空域法：直接对获取的数字图像进行处理。 频域法：对先对获取的数字图像进行正交变换，得到变换系数阵列，然后再进行处理，最后再逆变换到空 间域，得到图像的处理结果 2. 图像处理的主要内容是什么 答：图形数字化（图像获取）：把连续图像用一组数字表示，便于用计算机分析处理。图像变换：对图像进 行正交变换，以便进行处理。图像增强：对图像的某些特征进行强调或锐化而不增加图像的相关数据。图 像复原：去除图像中的噪声干扰和模糊，恢复图像的客观面目。图像编码：在满足一定的图形质量要求下 对图像进行编码，可以压缩表示图像的数据。图像分析：对图像中感兴趣的目标进行检测和测量，从而获 得所需的客观信息。图像识别：找到图像的特征，以便进一步处理。图像理解：在图像分析的基础上得出 对图像内容含义的理解及解释，从而指导和规划行为。 3. 名词解释：灰度、像素、图像分辨率、图像深度、图像数据量。 答：像素：在卫星图像上，由卫星传感器记录下的最小的分立要素(有空间分量和谱分量两种)。通常，表 示图像的二维数组是连续的，将连续参数 x,y ，和 f 取离散值后，图像被分割成很多小的网格，每个网格 即为像素 图像分辨率：指对原始图像的采样分辨率，即图像水平或垂直方向单位长度上所包含的采样点 数。单位是“像素点/单位长度” 图像深度是指存储每个像素所用的位数,也用于量度图像的色彩分辨率.图像深度确定彩色图像的每个像素 可能有的颜色数,或者确定灰度图像的每个像素可能有的灰度级数.它决定了彩色图像中可出现的最多颜色 数,或灰度图像中的最大灰度等级（图像深度：位图图像中，各像素点的亮度或色彩信息用二进制数位来表 示，这一数据位的位数即为像素深度，也叫图像深度。图像深度越深，能够表现的颜色数量越多，图像的 色彩也越丰富。） 图像数据量：图像数据量是一幅图像的总像素点数目与每个像素点所需字节数的乘积。 4. , 5. 什么是采样与量化 答：扫描：按照一定的先后顺序对图像进行遍历的过程。采样：将空间上连续的图像变成离散点的操作。 采样过程即可看作将图像平面划分成网格的过程。量化：将采样得到的灰度值转换为离散的整数值。灰度 级：一幅图像中不同灰度值的个数。一般取0~255，即256个灰度级 5.说明图像函数 的各个参数的具体含义。 答：其中，x 、y 、z 是空间坐标，λ是波长，t 是时间，I 是像素点的强度。它表示活动的、彩色的、三维 的视频图像。对于静止图像，则与时间t 无关；对于单色图像，则波长λ为常数；对于平面图像，则与坐 标z 无关。 1.请解释马赫带效应，马赫带效应和同时对比度反映了什么共同的问题 答：马赫带效应：基于视觉系统有趋向于过高或过低估计不同亮度区域边界值的现象。同时对比度现象： 此现象表明人眼对某个区域感觉到的亮度不仅仅依赖它的强度，而与环境亮度有关 共同点： 它们都反映了人类视觉感知的主观亮度并不是物体表面照度的简单函数。 2. 色彩具有那几个基本属性描述这些基本属性的含义。 答：色彩是光的物理属性和人眼的视觉属性的综合反映。色彩具有三个基本属性：色调、饱和度和亮度 色调是与混合光谱中主要光波长相联系的（红绿蓝）饱和度表示颜色的深浅程度，与一定色调的纯度有关， 纯光谱色是完全饱和的，随着白光的加入饱和度逐渐减少。（如深红、浅红等）亮度与物体的反射率成正比。 颜色中掺入白色越多就越明亮，掺入黑色越多亮度越小。 { 3.什么是视觉的空间频率特性什么是视觉的时间特性 答：视觉的空间频率特性：空间频率是指视像空间变化的快慢。明亮的图像（清晰明快的画面）意味着有 ),,,,(t z y x f I λ=

图像处理英文翻译

数字图像处理英文翻译 （Matlab帮助信息简介） xxxxxxxxx xxx Introduction MATLAB is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numeric computation. Using the MATLAB product, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and Fortran. You can use MATLAB in a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis, and computational biology. Add-on toolboxes (collections of special-purpose MATLAB functions, available separately) extend the MATLAB environment to solve particular classes of problems in these application areas. The MATLAB system consists of these main parts: Desktop Tools and Development Environment This part of MATLAB is the set of tools and facilities that help you use and become more productive with MATLAB functions and files. Many of these tools are graphical user interfaces. It includes: the

数字图像处理 外文翻译 外文文献 英文文献 数字图像处理

Digital Image Processing 1 Introduction Many operators have been proposed for presenting a connected component n a digital image by a reduced amount of data or simplied shape. In general we have to state that the development, choice and modi_cation of such algorithms in practical applications are domain and task dependent, and there is no \best method". However, it is interesting to note that there are several equivalences between published methods and notions, and characterizing such equivalences or di_erences should be useful to categorize the broad diversity of published methods for skeletonization. Discussing equivalences is a main intention of this report. 1.1 Categories of Methods One class of shape reduction operators is based on distance transforms. A distance skeleton is a subset of points of a given component such that every point of this subset represents the center of a maximal disc (labeled with the radius of this disc) contained in the given component. As an example in this _rst class of operators, this report discusses one method for calculating a distance skeleton using the d4 distance function which is appropriate to digitized pictures. A second class of operators produces median or center lines of the digital object in a non-iterative way. Normally such operators locate critical points _rst, and calculate a speci_ed path through the object by connecting these points. The third class of operators is characterized by iterative thinning. Historically, Listing [10] used already in 1862 the term linear skeleton for the result of a continuous deformation of the frontier of a connected subset of a Euclidean space without changing the connectivity of the original set, until only a set of lines and points remains. Many algorithms in image analysis are based on this general concept of thinning. The goal is a calculation of characteristic properties of digital objects which are not related to size or quantity. Methods should be independent from the position of a set in the plane or space, grid resolution (for digitizing this set) or the shape complexity of the given set. In the literature the term \thinning" is not used

基于小波变换的数字图像处理

基于小波变换的数字图像处理（MATLAB源代码） clear all; close all; clc; M=256;%原图像长度 N=64; %水印长度 [filename1,pathname]=uigetfile('*.*','select the image'); image1=imread(num2str(filename1)); subplot(2,2,1);imshow(image1); title('original image'); % orginal image for watermarking image1=double(image1); imagew=imread('dmg2.tif'); subplot(2,2,2);imshow(imagew);title('original watermark'); %original watermark %嵌入水印 [ca,ch,cv,cd] = dwt2(image1,'db1'); [cas,chs,cvs,cds] = dwt2(ca,'db1'); for i=1:N for j=1:N if imagew(i,j)==0 a=-1; else a=1; end Ca(i,j)=cas(i,j)*(1+a*0.03); end end IM= idwt2(Ca,chs,cvs,cds,'db1') ; markedimage=double(idwt2(IM,ch,cv,cd,'db1')); %显示嵌入后水印图像 subplot(2,2,3);colormap(gray(256));image(markedimage);title('marked image'); imwrite(markedimage,gray(256),'watermarked.bmp','bmp'); %提取水印 image1=imread(num2str(filename1));image1=double(image1); imaged=imread('watermarked.bmp'); [ca,ch,cv,cd] = dwt2(image1,'db1'); [cas,chs,cvs,cds]=dwt2(ca,'db1'); [caa,chh,cvv,cdd]=dwt2(imaged,'db1'); [caas,chhs,cvvs,cdds]=dwt2(caa,'db1'); for p=1:N for q=1:N

数字图像处理第三版中文答案 冈萨雷斯

第二章 2.1（第二版是0.2和1.5*1.5的矩形，第三版是0.3和1.5圆形） 对应点的视网膜图像的直径x 可通过如下图题2.1所示的相似三角形几何关系得到，即 ()()017 023 02.x .d = 解得x=0.06d 。根据2.1 节内容，我们知道：如果把中央凹处想象为一个有337000 个成像单元的圆形传感器阵列，它转换成一个大小2 5327.?π成像单元的阵列。假设成像单元之间的间距相等，这表明在总长为1.5 mm （直径） 的一条线上有655个成像单元和654个成像单元间隔。则每个成像单元和成像单元间隔的大小为s=[(1.5 mm)/1309]=1.1×10-6 m 。 如果在中央凹处的成像点的大小是小于一个可分辨的成像单元，在我们可以认为改点对于眼睛来说不可见。换句话说， 眼睛不能检测到以下直径的点： m .d .x 61011060-?<=，即m .d 610318-?< 2.2 当我们在白天进入一家黑暗剧场时，在能看清并找到空座时要用一段时间适应。2.1节描述的视觉过程在这种情况下起什么作用？ 亮度适应。 2.3 虽然图2.10中未显示，但交流电的却是电磁波谱的一部分。美国的商用交流电频率是77HZ 。问这一波谱分量的波长是多少？ 光速c=300000km/s ，频率为77Hz 。 因此λ=c/v=2.998 * 108(m/s)/77(1/s) = 3.894*106m = 3894 Km. 2.5 根据图2.3得：设摄像机能看到物体的长度为x (mm)，则有:500/x=35/14; 解得：x=200，所以相机的分辨率为：2048/200=10;所以能解析的线对为：10/2=5线对/mm. 2.7 假设中心在（x0,y0）的平坦区域被一个强度分布为： ])0()0[(2 2),(y y x x Ke y x i -+--= 的光源照射。为简单起见，假设区域的反射是恒定 的，并等于1.0，令K=255。如果图像用k 比特的强度分辨率进行数字化，并且眼睛可检测相邻像素间8种灰度的突变，那么k 取什么值将导致可见的伪轮廓？ 解：题中的图像是由： ()()()()()[ ]()()[]2 02 02 020********y y x x y y x x e .e y ,x r y ,x i y ,x f -+---+--=?== 一个截面图像见图（a ）。如果图像使用k 比特的强度分辨率，然后我们有情况见图（b ），其中()k G 21255+=?。因为眼睛可检测4种灰度突变，因此，k G 22564==?，K= 6。

外文翻译----数字图像处理与边缘检测

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中英文资料对照外文翻译 Digital Image Processing and Edge Detection Digital Image Processing Interest in digital image processing methods stems from two principal applica- tion areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for au- tonomous machine perception. An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image. Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spec- trum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra- sound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of applications. There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vi- sion, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than

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数字图像处理课件(冈萨雷斯第三版)复习材料

(1) 名词解释 RGB Red Green Blue，红绿蓝三原色 CMYK Cyan Magenta yellow blacK , 用于印刷的四分色 HIS Horizontal Situation Indicator 水平位置指示器 FFT Fast Fourier Transform Algorithm (method) 快速傅氏变换算法 CWT continuous wavelet transform 连续小波变换 DCT Discrete Cosine Transform 离散余弦变换 DWT DiscreteWaveletTransform 离散小波变换 CCD Charge Coupled Device 电荷耦合装置 Pixel: a digital image is composed of a finite number of elements,each of which has a particular lication and value,these elements are called pixel像素 DC component in frequency domain 频域直流分量 GLH Gray Level Histogram 灰度直方图 Mather(basic)wavelet：a function (wave) used to generate a set of wavelets, 母小波，用于产生小波变换所需的一序列子小波 Basis functions basis image 基函数基图像 Multi-scale analysis 多尺度分析 Gaussian function 高斯函数 sharpening fi l ter 锐化滤波器 Smoothing filter/convolution 平滑滤波器/卷积 Image enhancement /image restoration 图像增强和图像恢复 （2）问答题 1. Cite one example of digital image processing Answer:In the domain of medical image processing we may need to inspect a certain class of images generated by an electron microscope to eliminate bright, isolated dots that are no interest. 2.Cite one example of frequency domain operation from the following processing result, make a general comment about ideal highpass filter (figure B) and Gaussian highpass filter(figure D) A.Original image B.ideal highpass filter In contrast to the ideal low pass filter, it is to let all the signals above the cutoff frequency fc without loss, and to make all the signals below the cutoff frequency of FC without loss of. C.the result of ideal highpass filter D.Gaussian highpass filter High pass filter, also known as "low resistance filter", it is an inhibitory spectrum of the low frequency signal and retain high frequency signal model (or device). High pass filter can make the high frequency components, while the high-frequency part of the frequency in the image of the sharp change in the gray area, which is often the edge of the object. So high pass filter can make the image get sharpening processing E.The result of Gaussian filter