数字图像处理外文翻译参考文献
数字图像处理外文翻译参考文献
(文档含中英文对照即英文原文和中文翻译)
原文：
Application Of Digital Image Processing In The Measurement
Of Casting Surface Roughness
Ahstract- This paper presents a surface image acquisition system based on digital image processing technology. The image acquired by CCD is pre-processed through the procedure of image editing, image equalization, the image binary conversation and feature parameters extraction to achieve casting surface roughness measurement. The three-dimensional evaluation method is taken to obtain the evaluation parameters
and the casting surface roughness based on feature parameters extraction. An automatic detection interface of casting surface roughness based on MA TLAB is compiled which can provide a solid foundation for the online and fast detection of casting surface roughness based on image processing technology.
Keywords-casting surface; roughness measurement; image processing; feature parameters
Ⅰ.INTRODUCTION
Nowadays the demand for the quality and surface roughness of machining is highly increased, and the machine vision inspection based on image processing has become one of the hotspot of measuring technology in mechanical industry due to their advantages such as non-contact, fast speed, suitable precision, strong ability of anti-interference, etc [1,2]. As there is no laws about the casting surface and the range of roughness is wide, detection parameters just related to highly direction can not meet the current requirements of the development of the photoelectric technology, horizontal spacing or roughness also requires a quantitative representation. Therefore, the three-dimensional evaluation system of the casting surface roughness is established as the goal [3,4], surface roughness measurement based on image processing technology is presented. Image preprocessing is deduced through the image enhancement processing, the image binary conversation. The three-dimensional roughness evaluation based on the feature parameters is performed . An automatic detection interface of casting surface roughness based on MA TLAB is compiled which provides a solid foundation for the online and fast detection of casting surface roughness.
II. CASTING SURFACE IMAGE ACQUISITION SYSTEM
The acquisition system is composed of the sample carrier, microscope, CCD camera, image acquisition card and the computer. Sample carrier is used to place tested castings. According to the experimental requirements, we can select a fixed carrier and the sample location can be manually transformed, or select curing specimens and the position of the sampling stage can be changed. Figure 1 shows the whole processing procedure.,Firstly,the detected castings should be placed in the illuminated backgrounds as far as possible, and then through regulating optical lens, setting the CCD camera resolution and exposure time, the pictures collected by CCD are saved to computer memory through the acquisition card. The image preprocessing and feature value extraction on casting surface based on corresponding software are followed. Finally the detecting result is output.
III. CASTING SURFACE IMAGE PROCESSING
Casting surface image processing includes image editing, equalization processing, image enhancement and the image binary conversation,etc. The original and clipped images of the measured casting is given in Figure 2. In which a) presents the original image and b) shows the clipped image.
A.Image Enhancement
Image enhancement is a kind of processing method which can highlight certain image information according to some specific needs and weaken or remove some unwanted informations at the same time[5].In order to obtain more clearly contour of the casting surface equalization processing of the image namely the correction of the image histogram should be pre-processed before image segmentation processing. Figure 3 shows the original grayscale image and equalization processing image and their histograms. As shown in the figure, each gray level of the histogram has substantially the same pixel point and becomes more flat after gray equalization processing. The image appears more clearly after the correction and the contrast of the image is enhanced.
Fig.2 Casting surface image
Fig.3 Equalization processing image
B. Image Segmentation
Image segmentation is the process of pixel classification in essence. It is a very important technology by threshold classification. The optimal threshold is attained through the instmction thresh = graythresh (II). Figure 4 shows the image of the binary conversation. The gray value of the black areas of the Image displays the portion of the contour less than the threshold (0.43137), while the white area shows the gray value greater than the threshold. The shadows and shading emerge in the bright region may be caused by noise or surface depression.
Fig4 Binary conversation
IV. ROUGHNESS PARAMETER EXTRACTION
In order to detect the surface roughness, it is necessary to extract feature parameters of roughness. The average histogram and variance are parameters used to characterize the texture size of surface contour. While unit surface's peak area is parameter that can reflect the roughness of horizontal workpiece.And kurtosis parameter can both characterize the roughness of vertical direction and horizontal direction. Therefore, this paper establishes
histogram of the mean and variance, the unit surface's peak area and the steepness as the roughness evaluating parameters of the castings 3D assessment. Image preprocessing and feature extraction interface is compiled based on MATLAB. Figure 5 shows the detection interface of surface roughness. Image preprocessing of the clipped casting can be successfully achieved by this software, which includes image filtering, image enhancement, image segmentation and histogram equalization, and it can also display the extracted evaluation parameters of surface roughness.
Fig.5 Automatic roughness measurement interface
V. CONCLUSIONS
This paper investigates the casting surface roughness measuring method based on digital Image processing technology. The method is composed of image acquisition, image enhancement, the image binary conversation and the extraction of characteristic parameters of roughness casting surface. The interface of image preprocessing and the extraction of roughness evaluation parameters is compiled by MA TLAB which can provide a solid foundation for the online and fast detection of casting surface roughness.
REFERENCE
[1] Xu Deyan, Lin Zunqi. The optical surface roughness research pro gress and direction
[1]. Optical instruments 1996, 18 (1): 32-37.
[2] Wang Yujing. Turning surface roughness based on image measurement [D]. Harbin:
Harbin University of Science and Technology
[3] BRADLEY C. Automated surface roughness measurement[1]. The International
Journal of Advanced Manufacturing Technology ,2000,16(9) :668-674.
[4] Li Chenggui, Li xing-shan, Qiang XI-FU 3D surface topography measurement method
[J]. Aerospace measurement technology, 2000, 20(4): 2-10.
[5] Liu He. Digital image processing and application [ M]. China Electric Power Press,
2005
译文：
数字图像处理在铸件表面粗糙度测量中的应用
摘要—本文提出了一种表面图像采集基于数字图像处理技术的系统。由CCD获得的图像的步骤是通过预先处理图像编辑，图像均衡，图像二进制对话和特征参数的提取，实现铸件表面粗糙度测量。三维评价方法是得到评价参数和铸件表面粗糙度的特征参数的提取。一种基于MA TLAB的铸造表面粗糙度自动检测接口程序，可以提供一个坚实的基础在线和快速的基于图像处理技术的铸造表面粗糙度检测。
关键词—铸造表面粗糙度测量；图像处理；特征参数
Ⅰ.介绍
如今在质量和加工表面粗糙度的高度增加的需求下，由于如非接触，热点速度快，适用于精度高，抗干扰能力强等的优点，基于图像处理的机器视觉检测已成为机械工业中主要测量技术之一[1,2]。由于没有规定和限制，铸件表面粗糙度的范围是广泛的，检测参数与高度方向光电技术的发展，不能满足目前的要求，水平间距或粗糙度也需要一个定量表示。因此，基于图像处理技术的表面粗糙度测量方法，对铸造表面粗糙度建立三维评价体系为目标[ 3,4 ]。通过图像增强处理，推导出图像的预处理和图像二值谈话。三维粗糙度是基于特征参数进行评价的。一种基于MA TLAB的铸造表面粗糙度自动检测界面的编制提供了坚实的在线快速铸造表面粗糙度检测。
Ⅱ.铸件表面图像采集系统
采集系统由采样载体，显微镜，CCD摄像头，图像采集卡和计算机组成。样品载体是用来测试铸件。根据实验要求，我们可以选择一个固定的载体，采样位置可以手动转换，选择固化试样与采样阶段的位置是可以改变的。图1显示了整个加工过程，首先，检测到铸件应尽可能放置在明亮的背景下，然后通过调节光学透镜，
设置CCD摄像机分辨率和曝光时间，对CCD采集到的图片通过采集卡保存到计算机内存。根据相应的软件对铸件表面进行图像预处理和特征值提取，最后检测结果输出。
图1 铸造图像采集系统
Ⅲ.铸件表面图像处理
铸件表面图像处理主要包括图像编辑，均衡处理，图像增强和图像二值谈话等。原始的图像测量铸件图2中给出。其中（a）显示了原始图像和（b)显示剪辑图像。
A.图像增强
图像增强是一种处理方法，可以突出某些图像信息,根据特定的需要同时可以削弱或删除一些不必要的信息[5]。为了获得更清楚轮廓的铸件表面均匀化处理的图像即校正图像的直方图应在图像分割处理前预先处理。图3显示了原始灰度图像及其直方图均衡化处理的图像。如图所示，每个灰度级的直方图具有基本相同的像素点，灰度均衡化处理后变得更加平。校正后的对比度增强的图像将变得更加清晰。
a) 原始图像 b) 修剪图像
图2 铸件表面图像
a) 灰度图像 b) 直方图
c)均衡图像d）均衡直方图
图3 均衡处理图像
B.图像分割
图像分割是在本质上的像素分类的过程。它是由阈值分类的一个非常重要的技术。最优阈值是通过instmction脱粒= graythresh（II）达到的。图4显示图像的二进制谈话。图中的黑色区域显示部分的轮廓的灰度值低于阈值（0.43137），而白色区域表示灰度值大于阈值。阴影和阴影在明亮的区域出现可能造成噪音或表面凹陷。
a) 灰度图像 b) 二值图像
图4 图像的二值化
Ⅳ.粗糙度参数提取
为了检测表面粗糙度，需要提取粗糙度特征参数。平均直方图和方差是用来描述表面轮廓纹理尺寸参数。而单位表面的峰面积参数能反映工件的粗糙度水平。峰度参数可以表征垂直方向和水平方向的粗糙度。因此，本文建立直方图的均值和方差，单位表面的峰面积和陡度作为粗糙度评价参数的铸件三维评价。图像预处理和特征提取的界面是基于MATLAB编制的。图5显示了表面粗糙度的检测接口。图像预处理通过这个软件成功地实现了可裁剪的铸造，其中包括图像滤波，图像增强，图像分割和直方图均衡化，而且还可以显示所提取的评价表面粗糙度参数。
图5自动粗糙度测量接口
V.结论
本文研究了铸件表面粗糙度测量方法的基础上的数字图像处理技术。该方法由图像采集，图像增强，图像二值的对话和铸件表面的粗糙度特征参数的提取组成。MA TLAB编译图像预处理和提取粗糙度评估参数的接口，它可以提供铸件表面粗糙度的在线和快速检测一个坚实的基础。
参考文献
[1] Xu Deyan, Lin Zunqi. The optical surface roughness research pro gress and direction
[1]. Optical instruments 1996, 18 (1): 32-37.
[2] Wang Yujing. Turning surface roughness based on image measurement [D]. Harbin:
Harbin University of Science and Technology
[3] BRADLEY C. Automated surface roughness measurement[1]. The International
Journal of Advanced Manufacturing Technology ,2000,16(9) :668-674.
[4] Li Chenggui, Li xing-shan, Qiang XI-FU 3D surface topography measurement method
[J]. Aerospace measurement technology, 2000, 20(4): 2-10.
[5] Liu He. Digital image processing and application [ M]. China Electric Power Press,
2005
人脸识别 面部 数字图像处理相关 中英对照 外文文献翻译 毕业设计论文 高质量人工翻译 原文带出处
人脸识别相关文献翻译，纯手工翻译，带原文出处 (原文及译文)如下 翻译原文来自 Thomas David Heseltine BSc. Hons. The University of York Department of Computer Science For the Qualification of PhD. — September 2005 - 《Face Recognition: Two-Dimensional and Three-Dimensional Techniques》 4 Two-dimensional Face Recognition 4.1 Feature Localization Before discussing the methods of comparing two facial images we now take a brief look at some at the preliminary processes of facial feature alignment. This process typically consists of two stages: face detection and eye localisation. Depending on the application, if the position of the face within the image is known beforehand (fbr a cooperative subject in a door access system fbr example) then the face detection stage can often be skipped, as the region of interest is already known. Therefore, we discuss eye localisation here, with a brief discussion of face detection in the literature review(section 3.1.1). The eye localisation method is used to align the 2D face images of the various test sets used throughout this section. However, to ensure that all results presented are representative of the face recognition accuracy and not a product of the performance of the eye localisation routine, all image alignments are manually checked and any errors corrected, prior to testing and evaluation. We detect the position of the eyes within an image using a simple template based method. A training set of manually pre-aligned images of feces is taken, and each image cropped to an area around both eyes. The average image is calculated and used as a template. Figure 4-1 - The average eyes. Used as a template for eye detection. Both eyes are included in a single template, rather than individually searching for each eye in turn, as the characteristic symmetry of the eyes either side of the nose, provides a useful feature that helps distinguish between the eyes and other false positives that may be picked up in the background. Although this method is highly susceptible to scale(i.e. subject distance from the camera) and also introduces the assumption that eyes in the image appear near horizontal. Some preliminary experimentation also reveals that it is advantageous to include the area of skin just
数字图像处理外文翻译参考文献
数字图像处理外文翻译参考文献 (文档含中英文对照即英文原文和中文翻译) 原文： Application Of Digital Image Processing In The Measurement Of Casting Surface Roughness Ahstract- This paper presents a surface image acquisition system based on digital image processing technology. The image acquired by CCD is pre-processed through the procedure of image editing, image equalization, the image binary conversation and feature parameters extraction to achieve casting surface roughness measurement. The three-dimensional evaluation method is taken to obtain the evaluation parameters and the casting surface roughness based on feature parameters extraction. An automatic detection interface of casting surface roughness based on MA TLAB is compiled which can provide a solid foundation for the online and fast detection of casting surface roughness based on image processing technology.
外文文献及翻译DigitalImageProcessingandEdgeDetection数字图像处理与边缘检测
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 originally anticipated. The area of image analysis (also called image understanding) is in be- tween image processing and computer vision. There are no clearcut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low-, mid-, and highlevel processes. Low-level processes involve primitive opera- tions such as image
图像增强参考文献
[1] 冈萨雷斯. 数字图像处理[M]. 电子工业出版社,2003. [2] 杨帆等. 数字图像处理与分析[M]. 北京：北京航空航天大学出版社，2007 [3] 马平. 数字图像处理和压缩[M]. 北京：电子工业出版社，2007 [4] 闫敬文. 数字图像处理[M]. 北京：国防工业出版社，2007 [5] 王慧琴. 数字图像处理. 北京:北京邮电出版社, 2006. [6] 阮秋琦. 数字图像处理学. 北京:电子工业出版社, 2001 [7] 何东健. 数字图像处理. 西安:西安电子科技大学出版社, 2003. [8] 王家文, 曹宇. MATLAB6.5图形图像处理. 北京:国防工业出版社, 2004. [9] 余成波. 数字图像处理及MATLAB实现. 重庆:重庆大学出版社, 2003. [10] 张志涌, 徐彦琴. MATLAB教程-基于6.x版本.北京航空航天大学出版社, 2001. [11] 夏德深, 傅德胜. 计算机图像处理及应用. 南京:东南大学出版社, 2004. [12] Simard P,Steinkraus D,Malvar H.On-line Adaptation Image Coding with a 2-d Tarp Filter. Proceedings of IEEE Data Compression Conference[J].2002.vol.8(1):23-32. [13] WangHong，LuLing，QueDaShun. Image Compression Based on Wavelet Transformand Veetor Quantization[J] .Beijing : Proceedings of the First International Confereneeon Maehine Leamingand Cybernetics，2002(5):35-41 [14] WangHong，LuLing，QueDaShun. Image Compression Based on Wavelet Transformand Veetor Quantization[D]. Beijing：Proeeedingsof the First Inter national Confereneeon Maehine Leamingand Cybernetics，2002 [15] YufangGao ,Yang Liu. Analysis of Compression Encoding about Digital Image[D].Beijing: Beijing University of Posts and Telecommunications,2003 [16] Jerome M. Sha Piro. Afast Technology for Identifying Zerotreesin the EZW Algorithm[J]，IEEET rans. Coef. Aeoustv Speech Signal Proeessing.1996(7):11-23 [1] 冈萨雷斯. 数字图像处理[M]. 电子工业出版社,2003. 摘要：本书是把图像处理基础理论论述与软件实践方法相结合的第一本书，它集成了冈萨雷斯和伍兹所著的《数字图像处理》一书中的重要内容和MathWorks 公司的图像处理工具箱。本书的特色在于它重点强调了怎样通过开发新代码来增强这些软件工具。本书在介绍MATLAB编程基础知识之后，讲述了图像处理的主要内容，具体包括亮度变换、线性和非线性空间滤波、频率域滤波、图像复原与配准、彩色图像处理、小波、图像数据压缩、形态学图像处理、图像分割、区域和边界表示与描述以及对象识别等。 [2] 杨帆等. 数字图像处理与分析[M]. 北京：北京航空航天大学出版社，2007 摘要：系统介绍数字图像处理与分析技术中所涉及的有代表性的思想、算法与应用，跟踪图像处理技术的发展前沿，以图像频域变换、图像增强、图像复原、图像几何变换、图像压缩编码、数学形态学及应用、图像分割技术、图像特征分析、图像配准与识别、实用数字图像处理与应用系统为主线，系统讲述图像处理与分析技术的理论基础、典型算法和应用实例。编写上力求系统性、实用性与先进性相结合，理论与实践相交融，既注重传统知识的讲授，又兼顾新技术、新成果的应用。 [3] 马平. 数字图像处理和压缩[M]. 北京：电子工业出版社，2007
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
图像处理工具设计论文
基于VB的数字图像处理技术的开发与研究 摘要：本文首先介绍数字图像处理技术的背景和意义，然后介绍基于VB的所完成的图像处理应用软件，具体介绍该软件的功能、结构及简单操作。接着介绍一下VB和Windows的API函数，最后着重介绍软件中一些图像处理技术，关键技术讲解利用VB中的API函数，并通过VB编程语言如何进行图像处理的实现，介绍在这个设计过程中碰到的难题及解决方法。 关键词: Visual Basic、API、图像处理、FFT
According to the VB arithmetic figure picture handles technical development and research Abstract:This text introduces first the arithmetic figure picture handles technical background with meaning, then introduction according to the VB a picture for completing handles to apply the software, introducing function, construction and simple operations of that software in a specific way.Introduce the VB immediately after with the Windows the function of API, emphasize to introduce finally the some picture in inside in software handles technique, the key technique explains in detail the exploitation the function of API in the VB, and passes the VB weave the distance language how to proceed the picture handles of realize, introduce the hard nut to crack run into in this design process and solute the method. Key words: Visual Basic、API、图像处理、FFT
(完整版)数字信号处理英文文献及翻译
数字信号处理 一、导论 数字信号处理（DSP）是由一系列的数字或符号来表示这些信号的处理的过程的。数字信号处理与模拟信号处理属于信号处理领域。DSP包括子域的音频和语音信号处理，雷达和声纳信号处理，传感器阵列处理，谱估计，统计信号处理，数字图像处理，通信信号处理，生物医学信号处理，地震数据处理等。 由于DSP的目标通常是对连续的真实世界的模拟信号进行测量或滤波，第一步通常是通过使用一个模拟到数字的转换器将信号从模拟信号转化到数字信号。通常，所需的输出信号却是一个模拟输出信号，因此这就需要一个数字到模拟的转换器。即使这个过程比模拟处理更复杂的和而且具有离散值，由于数字信号处理的错误检测和校正不易受噪声影响，它的稳定性使得它优于许多模拟信号处理的应用（虽然不是全部）。 DSP算法一直是运行在标准的计算机，被称为数字信号处理器（DSP）的专用处理器或在专用硬件如特殊应用集成电路（ASIC）。目前有用于数字信号处理的附加技术包括更强大的通用微处理器，现场可编程门阵列（FPGA），数字信号控制器（大多为工业应用，如电机控制）和流处理器和其他相关技术。 在数字信号处理过程中，工程师通常研究数字信号的以下领域：时间域（一维信号），空间域（多维信号），频率域，域和小波域的自相关。他们选择在哪个领域过程中的一个信号，做一个明智的猜测（或通过尝试不同的可能性）作为该域的最佳代表的信号的本质特征。从测量装置对样品序列产生一个时间或空间域表示，而离散傅立叶变换产生的频谱的频率域信息。自相关的定义是互相关的信号本身在不同时间间隔的时间或空间的相关情况。 二、信号采样 随着计算机的应用越来越多地使用，数字信号处理的需要也增加了。为了在计算机上使用一个模拟信号的计算机，它上面必须使用模拟到数字的转换器（ADC）使其数字化。采样通常分两阶段进行，离散化和量化。在离散化阶段，信号的空间被划分成等价类和量化是通过一组有限的具有代表性的信号值来代替信号近似值。 奈奎斯特-香农采样定理指出，如果样本的取样频率大于两倍的信号的最高频率，一个信号可以准确地重建它的样本。在实践中，采样频率往往大大超过所需的带宽的两倍。 数字模拟转换器（DAC）用于将数字信号转化到模拟信号。数字计算机的使用是数字控制系统中的一个关键因素。 三、时间域和空间域 在时间或空间域中最常见的处理方法是对输入信号进行一种称为滤波的操作。滤波通常包括对一些周边样本的输入或输出信号电流采样进行一些改造。现在有各种不同的方法来表征的滤波器，例如： 一个线性滤波器的输入样本的线性变换；其他的过滤器都是“非线性”。线性滤波器满足叠加条件，即如果一个输入不同的信号的加权线性组合，输出的是一个同样加权线性组合所对应的输出信号。
外文翻译
外文翻译 - 1 - 一种小波收缩去噪的参数选择方法 摘要： 标准正交小波基的阈值估计对于高斯去噪来说是一个很好的工具。然而，由Donoho 和Johnstone 给出的通用阈值会导致“过扼杀”小波系数的倾向。 对于去噪问题，本文主要介绍由Chambolle 等提出的确定的方法，这种方法把去噪问题作为变差问题来研究。这个问题的解可以根据小波收缩方法明确地表达出来。这使我们能成功地运用小波收缩方法去解决更一般地去噪问题，并为收缩参数的选择提出一种新的准则，即H －曲线准则。基于这种想法， 对于不同的参数值)(111L B 的范数的计算值，它与以对数形式考虑的2L 的范数 的剩余相对应。大量的数字实验表明这种新的收缩参数的选择方法对于高斯噪声以及其它种类的噪声有很好的去噪效果。 中图法分类号（2000）：65J20，65D99，44A15，60G35，68U10. 关键词：去噪，变差方法，小波收缩，参数选择，H －曲线. 1. 引言 在平方域I 中，用一个函数),(y x f 来表示带噪图像，求出f 的近似函数的问题，即找到一个表示原始的未被扰乱的图像的函数f ，这个问题可被下面的变差问题所替代。λ为一个正参数，在特定空间Y 中的所有可能函数中找一个最小的函数 *λg Y I L g g f ||||2||||2)(2λ+- （1.1） ?-=-I I L dxdy y x g y x f g f 2/12)()|),(),(|(:||||2 （1.2） 其中用在)(2I L 中的范数来计算f 与g 之间的误差，Y g ||||是近似函数g 在平滑空间Y 中的范数。 这个问题的解很大程度上依赖于函数空间Y 的选择。拥有许多优点的空间是根据g 的小波系数能够表示g 的范数的空间。另外，对于空间Y 的选择应该是一个包含不同于平滑性函数的空间，这样的空间能很好的表现真实的 图像。例如，在许多图像处理的文章中常出现Besov 空间))((111I L B 。而且， 这空间在区间I 与有界变差函数空间相似，)(I BV 对于数字图像处理是非常 有用的（详见[16]和[17]）。因为)(I BV 包含在))((111I L B 中，因此我们有 )(()())((11111I L B I BV I L B ∞?? (1.3) 如果想了解Besov 空间的确切定义，有兴趣的读者请查阅文献[4]。在文献[1] 中所提到的))((111I L B Y =可以让读者根据f 的小波系数的适当收缩去确定变 差问题（1.1）的确切解。更确切的说，小波收缩得到的确切解使得系数的绝对值大于收缩参数λ，并且阈值趋向于将所有小波系数置零。 基于小波收缩的非线性算法最初是由Donoho 和Johnstone 提出的，这种
数字图像处理实验报告 (2)
数字图像处理实验报告实验一数字图像基本操作及灰度调整 一、实验目得 1)掌握读、写图像得基本方法。 2)掌握MＡTLAB语言中图像数据与信息得读取方法。 3)理解图像灰度变换处理在图像增强得作用。 4)掌握绘制灰度直方图得方法,理解灰度直方图得灰度变换及均衡化得方法。 二、实验内容与要求 1.熟悉ＭATLＡB语言中对图像数据读取,显示等基本函数 特别需要熟悉下列命令:熟悉imｒeａｄ()函数、ｉmwｒｉte()函数、size(）函数、Ｓｕbpｌot()函数、Figｕre()函数。 1)将MA TＬAB目录下ｗork文件夹中得foｒｅｓｔ、tif图像文件读出、用到imｒ ead,imｆｉnfo 等文件,观察一下图像数据,了解一下数字图像在MＡTLAB中得处理就就是处理一个矩阵。将这个图像显示出来(用imshow)。尝试修改maｐ颜色矩阵得值,再将图像显示出来,观察图像颜色得变化。 2)将MＡTＬAB目录下ｗoｒk文件夹中得b7４7、jpg图像文件读出,用rgb2ｇｒay （)将其 转化为灰度图像,记为变量Ｂ。 2.图像灰度变换处理在图像增强得作用 读入不同情况得图像,请自己编程与调用Ｍatlab函数用常用灰度变换函数对输入图像进行灰度变换,比较相应得处理效果。 3.绘制图像灰度直方图得方法,对图像进行均衡化处理 请自己编程与调用Maｔlab函数完成如下实验。 1)显示B得图像及灰度直方图，可以发现其灰度值集中在一段区域，用imadjuｓｔ 函 数将它得灰度值调整到[0，1]之间,并观察调整后得图像与原图像得差别,调整后得灰度直方图与原灰度直方图得区别。 2)对Ｂ进行直方图均衡化处理,试比较与源图得异同。 3)对B进行如图所示得分段线形变换处理,试比较与直方图均衡化处理得异同。
外文翻译---一个索贝尔图像边缘检测算法描述
译文一： 1一个索贝尔图像边缘检测算法描述[1] 摘要：图像边缘检测是一个确定图像边缘的过程，在输入的灰度图中的各个点寻找绝对梯度近似级对于边缘检测是非常重要的。为边缘获得适当的绝对梯度幅度主要在与使用的方法。Sobel算子就是在图像上进行2-D的空间梯度测量。转换2-D像素列阵到性能统计数据集提高了数据冗余消除，因此，作为代表的数字图像，数据量的减少是需要的。Sobel边缘检测器采用一对3×3的卷积模板，一块估计x－方向的梯度，另一块估计y－方向的梯度。Sobel检测器对于图像中的噪音很敏感，它能有效地突出边缘。因此，Sobel算子被建议用在数据传输中的大量数据通信。 关键词：图像处理，边缘检测，Sobel算子，通信数据，绝对梯度幅度。 引言 图像处理在现代数据储存和数据传输方面十分重要，特别是图像的渐进传输，视频编码(电话会议)，数字图书馆，图像数据库以及遥感。它与处理靠算法产生所需的图像有关(Milan et al., 2003)。数字图像处理(DSP)提高了在极不利条件下所拍摄的图像的质量，具体方法有：调整亮度与对比度，边缘检测，降噪，调整重点，减少运动模糊等(Gonzalez, 2002)。图像处理允许更广泛的范围被应用到输入数据，以避免如噪声和信号失真集结在加工过程中存在的问题(Baker & Nayar, 1996)。在19世纪60年代的Jet Propulsion实验室，美国麻省理工学院(MIT)，贝尔实验室以及一些其他的地方，数字图像处理技术不断发展。但是，因为当时的计算设备关系，处理的成本却很高。随着20世纪快速计算机和信号处理器的应用，数字图像处理变成了图像处理最通用的形式，因为它不只是最多功能的，还是最便宜的。图像处理过程中允许一些更复杂算法的使用，从而可以在简单任务中提供更先进的性能，同时可以实现模拟手段不能实现的方法(Micheal, 2003)。因此，计算机搜集位表示像素或者点形成的图片元素，以此储存在电脑中(Vincent, 2006)。首先，图像是在空间上的参数测量，而大多数的信号是在时间上的参数测量。其次，它们包含了大量的信息(Guthe和Strasser, 2004)；图像处理是当输入是图像时的信息处理方式，就像是帧视频；输出不一定是 [1] A Descriptive Algorithm for Sobel Image Edge Detection[C]. Proceedings of Informing Science & IT Education Conference (InSITE) 2009: 97-107.
数字图像处理论文中英文对照资料外文翻译文献
中英文对照资料外文翻译文献 原文 To image edge examination algorithm research Abstract ：Digital image processing took a relative quite young discipline, is following the computer technology rapid development, day by day obtains the widespread application.The edge took the image one kind of basic characteristic, in the pattern recognition, the image division, the image intensification as well as the image compression and so on in the domain has a more widespread application.Image edge detection method many and varied, in which based on brightness algorithm, is studies the time to be most long, the theory develops the maturest method, it mainly is through some difference operator, calculates its gradient based on image brightness the change, thus examines the edge, mainly has Robert, Laplacian, Sobel, Canny, operators and so on LOG。 First as a whole introduced digital image processing and the edge detection survey, has enumerated several kind of at present commonly used edge detection technology and the algorithm, and selects two kinds to use Visual the C language programming realization, through withdraws the image result to two algorithms the comparison, the research discusses their good and bad points. Foreword：In image processing, as a basic characteristic, the edge of theimage, which is widely used in the recognition, segmentation,intensification and compress of the image, is often applied tohigh-level domain.There are many kinds of ways to detect the edge. Anyway, there aretwo main techniques: one is classic method based on the gray grade ofevery pixel; the other one is based on wavelet and its multi-scalecharacteristic. The first method, which is got the longest research,get the edge according to the variety of the pixel gray.
外文翻译----数字图像处理和模式识别技术关于检测癌症的应用
引言 英文文献原文 Digital image processing and pattern recognition techniques for the detection of cancer Cancer is the second leading cause of death for both men and women in the world , and is expected to become the leading cause of death in the next few decades . In recent years , cancer detection has become a significant area of research activities in the image processing and pattern recognition community .Medical imaging technologies have already made a great impact on our capabilities of detecting cancer early and diagnosing the disease more accurately . In order to further improve the efficiency and veracity of diagnoses and treatment , image processing and pattern recognition techniques have been widely applied to analysis and recognition of cancer , evaluation of the effectiveness of treatment , and prediction of the development of cancer . The aim of this special issue is to bring together researchers working on image processing and pattern recognition techniques for the detection and assessment of cancer , and to promote research in image processing and pattern recognition for oncology . A number of papers were submitted to this special issue and each was peer-reviewed by at least three experts in the field . From these submitted papers , 17were finally selected for inclusion in this special issue . These selected papers cover a broad range of topics that are representative of the state-of-the-art in computer-aided detection or diagnosis(CAD)of cancer . They cover several imaging modalities(such as CT , MRI , and mammography) and different types of cancer (including breast cancer , skin cancer , etc.) , which we summarize below . Skin cancer is the most prevalent among all types of cancers . Three papers in this special issue deal with skin cancer . Y uan et al. propose a skin lesion segmentation method. The method is based on region fusion and narrow-band energy graph partitioning . The method can deal with challenging situations with skin lesions , such as topological changes , weak or false edges , and asymmetry . T ang proposes a snake-based approach using multi-direction gradient vector flow (GVF) for the segmentation of skin cancer images . A new anisotropic diffusion filter is developed as a preprocessing step . After the noise is removed , the image is segmented using a GVF 1