外文翻译--数字图像处理方法的研究

1、.附录附录A外文资料The research of digital image processing techniqueIntroductionInterest 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 f

2、or autonomous machine perception. This chapter has several objectives: (1)to define the scope ofthe field that we call image processing; (2)to give a historical perspective of the origins of this field; (3)to give an idea of the state of the art in image processing by examining some of the principal

3、 area in which it is applied; (4)to discuss briefly the principal approaches used in digital image processing; (5)to give an overview of the components contained in a typic叭general-purpose image processing system; and (6) to provide direction to the books and other literature where image processing

4、work normally is reporter.What Is Digital Image Processing?An image may be defined as a two-dimensional function,f(x,y)，where x and y arespatial (plane) coordinates, and the amplitude of fat any pair of coordinates (x,y) is called the intensity or gray level of the image at that point. When x, y, an

5、d digital image. Tire 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, ima

6、ge 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 human who are limited to the visual band

7、 of the electromagnetic (E11)spectrum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that human are not accustomed to associating with image. these include ultrasound, electron microscopy, and computer-generat

8、ed images. Thus, digital image processing encompasses a wide and varied field of application.There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vision, start ,sometimes a distinction is made by defining imag

9、e 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 tire average intensity of an image (which yields a single number) would n

10、ot be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computer toemulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of art

11、ificial intelligence (AI) whose objective is to emulate human intelligence. This 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 between ima

12、ge processing and computer vision.There are no clear-cut 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 is this continuum: how-，mid-, and high-ever processes. low-level pro

13、cesses involve primitive operation such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its input and output are images.mid-level processing on images involves tasks such as segmentation (partitioning an i

14、mage into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual object.amid-level process is characterized by the fact that its inputs generally are images,but its output is attributes extracted fro

15、m those images (e. g., edges contours, and the identity of individual object). Finally, higher-level processing involves "making sense"of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive function normally associated wit

16、h vision. Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing encompasses processes whose inputs and outpu

17、ts are images and, in addition,encompasses processes that extract attributes from images,up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area con

18、taining the text. Preprocessing that images, extracting (segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of th

19、e content of the page may be viewed as being in the domain of image analysis and even computer vision,depending on the level of complexity implied by the statement "making cense." As will become evident shortly, digital image processing, as we have defined it, is used successfully in a bro

20、ad rang of areas of exceptional social and economic value. The concepts developed in the following chapters are the foundation for the methods used in those application areas.The Origins of Digital Image ProcessingOne of the first applications of digital images was in the newspaper industry, when pi

21、cturesfirst sent by submarine cable between London and NewYork.Introduction of the Bartlane cable picture transmission system in the early 1920s reduced the time required to transport a picture across the Atlantic from more than a week to less than three hours.Specialized printing equipment coded pi

22、ctures for cable transmission and then recondstruced on a telegragh printer fitted with typefaces simulating a halftone pattern.The idea of computer goes back to the invention of the abacus in Asia Mintor,more than 5000 years ago. More recently, there were developments in the past two centuries that

23、 are the foundation of what we call computer today. However, the basisfor what we call a modem digital computer dates back to only the 1940s with theintroduction by John von Neumann of two key concepts: (1) a memory to hold a stored program and data, and (2)conditional branching. There two ideas are

24、 the foundation of a central processing unit (CPU), which is at the heart of computer today. Starting with von Neumann, there were a series of advances that led to computers powerful enough to be used for digital image processing. Briefly, these advances may be summarized as follow:(1) the invention

25、 of the transistor by Bell Laboratories in 1948; (2) the development in the 1950s and 1960s of the high-level programming languages COBOL (Common Business-Oriented Language) and FORTRAII,( Formula Translator);(3) the invention of the integrated circuit (IC) at Texas Instruments in 1958;(4) the devel

26、opment of operating system in the early 1960s;(5)the development of the microprocessor (a single chip consisting of the central processing unit, memory, and input and output controls) by Inter in the early 1970s;(6) introduction by IBM ofthe personal computer in 1981;(7) progressive miniaturization

27、of components, starting with large scale integration (LI)in the late 1970s, then very large scale integration (VLSI) in the 1980s, to the present use of ultra large scale integration (ULSI).Concurrent with these advances were development in the areas of mass storage and display systems, both of whic

28、h are fundamental requirements for digital image processing. The first computers powerful enoueh to carry out meaningful image processing tasks appeared in the early 1960s. The birth of what we call digital image processing today can be traced to the availability of those machines and the onset of t

29、he apace program during that period. It took the combination of those two developments to bring into focus the potential of digital image processing concepts. Work on using computer techniques for improving images from a space probe began at the Jet Propulsion Laboratory (Pasadena, California) in 19

30、64 when pictures of the moon transmitted by Ranger 7 were processed by a computer to correct various types of image distortion inherent in the on-board television camera. FigurelAshows the fast image of the moon taken by Ranger 7 on July 31, 1964 at 9: 09 A. M. Eastern Daylight Time (EDT), about 17

31、minutes before impacting the lunar surface (the markers, called reseau mark, are used for geometric corrections, as discussed in Chapter 5). This also is the fast image of the moon taken by a U.S. spacecraft. The imaging lessons learned with ranger 7 served as the basis for improved methods used to

32、enhance and restore images from the Surveyor missions to the moon, the Mariner series of flyby mission to Mars, the Apollo manned flights to the moon, and others.In parallel with space application, digital image processing techniques began in the late 1960s and early 1970s to be used in medical imag

33、ing, remote Earth resources observations, and astronomy. The invention in the early 1970s of computerized axial tomography (CAT), also called computerized tomography (CT) for short, is one of the most important events in the application of image processing in medical diagnosis. Computerized axial to

34、mogmphy is a process in which a ring of detectors encircles an object (or patient) and an X-ray source, concentric with the detector ring, rotates about the object. The X-rays pass through the object and are collected at the opposite end by the corresponding detectors in the ring. As the source rota

35、tes, this procedure is repeated. Tomography consists of algorithms that use the sensed data to construct an image that represents a "slice" through the object. Motion of the object in a direction pe endicular to the ring of detectors produces a set of such slices, which constitute a three-

36、dimensional(3-D) redition of the inside of the object. Tomography was invented independently by Sir Godfrey N.Hounsfield and Professor Allan M.Cormack, who shared the X-rays were discovered in 1895. These two inventions nearly 100 years apart,led to some of the most active application areas of image

37、 processing today.From：数字图像处理方法的研究绪论数字图像处理方法的研究源于两个主要应用领域:其一是为了便于人们分析而对图像信息进行改进;其二是为了使机器自动理解而对图像数据进行存储、传输及显示。数字图像处理的概念一幅图像可定义为一个二维函数f(-, y),这里x和y是空间坐标，而在任何一对空间坐标f (x,y)上的幅值f称为该点图像的强度或灰度。当x, y和幅值f为有限的、离散的数值时，称该点是由有限的元素组成的，没一个元素都有一个特定的位置和幅值，这些元素称为图像元素、画面元素或象素。象素是广泛用于表示数字图像元素的词汇。在第二章，将用更正式的术语研究这些定义。视觉是

38、人类最高级的感知器官，所以，毫无疑问图像在人类感知中扮演着最重要的角色。然而，人类感知只限于电磁波谱的视觉波段，成像机器则刚覆盖几乎全部电磁波谱，从伽马射线无线电波。它们可以对非人类习惯的那些图像源进行加工，这些图像源包括超声波、电子显微镜及计算机产生的图像。因此，数字图像处理涉及各种各样的应用领域。图像处理涉及的范畴或其他相关领域(例如，图像分析和计算机视觉)的界定在初创人之间并没有一致的看法。有时用处理的输人和输出内容都是图像这一特点来界定图像处理的范围。我们认为这一定义仅是人为界定和限制。例如，在这个定义下，甚至最普通的计算一幅图像灰度平均值的工作都不能算做是图像处理。另一方面，有些领域

39、(如计算机视觉)研究的最高目标是用计算机去模拟人类视觉，包括理解和推理并根据视觉输人采取行动等。这一领域本身是人工智能的分支，其目的是模仿人类智能。人工智能领域处在其发展过程中的初期阶段，它的发展比预期的要慢得多，图像分析(也称为图像理解)领域则处在图像处理和计算机视觉两个学科之间。近十年来，用光信息处理技术来进行数据加密和保障数据安全引起了相当的关注。Pefregier和Javidi最早发表了这个领域的研究论文。由于光学信息处理系统的高度并行性和超快处理速度，光学安全（optical security）技术对信息安全技术的发展具有重要的理论意义和应用前景。另外，由于傅里叶光学信息处理系统具有

40、读写复振幅的能力，而该复振幅信息由于其相位部分在普通光源下是无法看到的，故不能用仅对光强敏感的探测器。因此利用光学信息处理对光学图像进行安全加密是一种行之有效的方法。1995 年， Philippe Refregier 等提出了双随机相位编码方法，这种方法具有较好的安全性和鲁棒性。从此光学加密技术进入快速发展时期。研究人员随后提出了基于分数傅里叶变换的加密方法、基于菲涅耳变换的加密方法、基于联合变换相关器的加密系统、利用离轴数字全息的加密系统和利用相移干涉技术的加密系统等大量新的或改进的加密系统，使得光学加密领域的研究异彩纷呈。虽然目前光学加密技术的发展方兴未艾，但其前景不可估量。总的来说，与

41、电子手段相比，现有的光学加密系统还存在一些缺点：可实施性、灵活性与稳定性都有待提高。从图像处理到计算机视觉这个连续的统一体内并没有明确的界线。然而，在这个连续的统一体中可以考虑三种典型的计算处理(即低级、中级和高级处理)来区分其中的各个学科。低级处理涉及初级操作，如降低噪声的图像预处理，对比度增强和图像尖锐化。低级处理是以输人、输出都是图像为特点的处理。中级处理涉及分割(把图像分为不同区域或目标物)以及缩减对目标物的描述，以使其更适合计算机处理及对不同日标的分类(识别)。中级图像处理是以输人为图像，但输出是从这些图像中提取的特征(如边缘、轮廓及不同物体的标识等)为特点的最后，高级处理涉及在图像

42、分析中被识别物体的总体理解，以及执行与视觉相关的识别函数(处在连续统一体边缘)等。根据上述讨论，我们看到，图像处理和图像分析两个领域合乎逻辑的重叠区域是图像中特定区域或物体的识别这一领域。这样，在本书中，我们界定数字图像处理包括输人和输出均是图像的处理，同时也包括从图像中提取特征及识别特定物体的处理。举一个简单的文本自动分析方面的例子来具体说明这一概念。在自动分析文本时首先获取一幅包含文本的图像，对该图像进行预处理，提取(分割)字符，然后以适合计算机处理的形式描述这些字符，最后识别这些字符，而所有这些操作都在本书界定的数字图像处理的范围内理解一页的内容可能要根据理解的复杂度从图像分析或计算机视觉领域考虑问题。这样，本书定义的数字图像处理的概念将在有特殊社会和经济价值的领域内通用在以下各章展开的概念是那些应用领域所用方法的基础。数字图