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The research of digital image processing technique
1 Introduction
Interest in digital image processing methods stems from two principal application ares:improvement of pictorial information for human interpretation;and processing of image data for storage,transmission,and representation for autonomous machine perception.
1.1 What Is Digital Image Processing?
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 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 referrer to as picture elements,image elements,pels,and pixels.Pixel is the term most widely used to denote the elements of a digital image.We consider these definitions in more formal terms in Chapter2.
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 of the electromagnetic (EM) 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-generated 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 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 computer 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. 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 image processing and computer vision.
There 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: low-, mid-, and high-ever processes. Low-level processes 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 image 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 from 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 with 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 outputs 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 containing 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 the 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 broad 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.
1.2 The Origins of Digital Image Processing
One of the first applications of digital images was in the newspaper industry, when pictures were first sent by submarine cable between London and New York.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 pictures for cable transmission and then reconstructed them at the receiving end. Figure 1.1 was transmitted in this way and reproduced on a telegraph printer fitted with typefaces simulating a halftone pattern.
Some of the initial problems in improving the visual quality of these early digital pictures were related to the selection of printing procedures and the distribution of intensity levels. The printing method used to obtain Fig. 1.1 was abandoned toward the end of 1921 in favor of a technique based on photographic reproduction made from tapes perforated at the telegraph receiving terminal. Figure 1.2 shows an images obtained using this method. The improvements over Fig. 1.1 are evident, both in tonal quality and in resolution.
FIGURE 1.2A digital picture made in 1922 from a tape punched after the signals had crossed the Atlantic twice. Some errors are Visible. (McFarlane)
FIGURE 1.1 A digital picture produced in
1921 from a coded tape by a telegraph printer With special type faces (McFarlane)
The early Bartlane systems were capable of coding images in five distinct level of gray.This capability was increased to 15 levels in 1929. Figure 1.3 is typical of the images that could be obtained using the 15-tone equipment. During this period,introduction of a system for developing a film plate via light beams that were modulated by the coded picture tape improved the reproduction process considerably.Although the examples just cited involve digital images, they are not considered digital image processing results in the context of our definition because computer were not involved in their creation. Thus, the history of digital processing is intimately tied to the development of the digital computer. In fact digital images require so much storage and computational power that progress in the field of digital image processing has been dependent on the development of digital computers of supporting technologies that include data storage, display, and transmission.
The idea of a computer goes back to the invention of the abacus in Asia Minor,more than 5000 years ago. More recently, there were developments in the past two centuries that are the foundation of what we call computer today. However, the basis for what we call a modern digital computer dates back to only the 1940s with the introduction 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 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 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 FORTRAN
( Formula Translator);
(3) the invention of the integrated circuit (IC) at Texas Instruments in 1958;
(4) the development 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 of the personal computer in 1981;
(7) progressive miniaturization 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).
Figure 1 .3 In 1929 from London to Cenerale Pershing that New York delivers with 15 level tone equipments through cable with Foch do not the photograph by decoration.
Concurrent with these advances were development in the areas of mass storage and display systems, both of which are fundamental requirements for digital image processing.The first computers powerful enough 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 the 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 1964 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. Figure1.4shows the first image of the moon taken by Ranger 7 on July 31, 1964 at 9: 09 A. M. Eastern Daylight Time (EDT), about 17 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 first 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 enhance and restore images from the Surveyor missions to the moon, the Mariner series of flyby mission to Mars, theApollo 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 imaging, 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 tomography 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 rotates, 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 perpendicular to the ring of detectors produces a set of such slices, which constitute a three-dimensional (3-D) rendition 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 by Wilhelm Conrad Roentgen, for which he received the 1901 Nobel Prize for Physics. These two inventions, nearly 100 years apart, led to some of the most active application areas of image processing today.
Figure 1.4 The first picture ofthe moon by a U.S. Spacecraft. Ranger 7 took this image on July 31, 1964 at 9: 09A.M. EDT, about 17 minutes before Impacting the lunar surface. (Courtesy o fNASA.)
數(shù)字圖像處理方法的研究
1 緒論
數(shù)字圖像處理方法的研究源于兩個主要應(yīng)用領(lǐng)域:其一是為了便于人們分析而對圖像信息進行改進；其二是為了使機器自動理解而對圖像數(shù)據(jù)進行存儲、傳輸及顯示。
1.1 數(shù)字圖像處理的概念
一幅圖像可定義為一個二維函數(shù) f(x, y)，這里x和y是空間坐標，而在任何一對空間坐標f(x, y)上的幅值f稱為該點圖像的強度或灰度。當(dāng)x，y和幅為f為有限的、離散的數(shù)值時，稱該點是由有限的元素組成的，沒一個元素都有一個特定的位置和幅值，這些元素稱為圖像元素、畫面元素或象素。象素是廣泛用于表示數(shù)字圖像元素的詞匯。在第二章，將用更正式的術(shù)語研究這些定義。視覺是人類最高級的感知器官，所以，毫無疑問圖像在人類感知中扮演著最重要的角色。然而，人類感知只限于電磁波譜的視覺波段，成像機器則可覆蓋幾乎全部電磁波譜，從伽馬射線到無線電波。它們可以對非人類習(xí)慣的那些圖像源進行加工，這些圖像源包括超聲波、電子顯微鏡及計算機產(chǎn)生的圖像。因此，數(shù)字圖像處理涉及各種各樣的應(yīng)用領(lǐng)域。
圖像處理涉及的范疇或其他相關(guān)領(lǐng)域(例如，圖像分析和計算機視覺)的界定在初創(chuàng)人之間并沒有一致的看法。有時用處理的輸人和輸出內(nèi)容都是圖像這一特點來界定圖像處理的范圍。我們認為這一定義僅是人為界定和限制。例如，在這個定義下，甚至最普通的計算一幅圖像灰度平均值的工作都不能算做是圖像處理。另一方面，有些領(lǐng)域(如計算機視覺)研究的最高目標是用計算機去模擬人類視覺，包括理解和推理并根據(jù)視覺輸人采取行動等。這一領(lǐng)域本身是人工智能的分支，其目的是模仿人類智能。人工智能領(lǐng)域處在其發(fā)展過程中的初期階段，它的發(fā)展比預(yù)期的要慢得多，圖像分析(也稱為圖像理解)領(lǐng)域則處在圖像處理和計算機視覺兩個學(xué)科之間。
從圖像處理到計算機視覺這個連續(xù)的統(tǒng)一體內(nèi)并沒有明確的界線。然而，在這個連續(xù)的統(tǒng)一體中可以考慮三種典型的計算處理(即低級、中級和高級處理)來區(qū)分其中的各個學(xué)科。低級處理涉及初級操作，如降低噪聲的圖像預(yù)處理，對比度增強和圖像尖銳化。低級處理是以輸人、輸出都是圖像為特點的處理。中級處理涉及分割〔 把圖像分為不同區(qū)域或目標物)以及縮減對目標物的描述，以使其更適合計算機處理及對不同日標的分類(識別)。中級圖像處理是以輸人為圖像，但輸出是從這些圖像中提取的特征(如邊緣、輪廓及不同物體的標識等)為特點的。最后，高級處理涉及在圖像分析中被識別物體的總體理解，以及執(zhí)行與視覺相關(guān)的識別函數(shù)(處在連續(xù)統(tǒng)一體邊緣)等。
根據(jù)上述討論，我們看到，圖像處理和圖像分析兩個領(lǐng)域合乎邏輯的重疊區(qū)域是圖像中特定區(qū)域或物體的識別這一領(lǐng)域。這樣，在本書中，我們界定數(shù)字圖像處理包括輸人和輸出均是圖像的處理，同時也包括從圖像中提取特征及識別特定物體的處理。舉一個簡單的文本自動分析方面的例子來具體說明這一概念。在自動分析文本時首先獲取一幅包含文本的圖像，對該圖像進行預(yù)處理，提取(分割)字符，然后以適合計算機處理的形式描述這些字符，最后識別這些字符，而所有這些操作都在本書界定的數(shù)字圖像處理的范圍內(nèi)。理解一頁的內(nèi)容可能要根據(jù)理解的復(fù)雜度從圖像分析或計算機視覺領(lǐng)域考慮問題。這樣，本書定義的數(shù)字圖像處理的概念將在有特殊社會和經(jīng)濟價值的領(lǐng)域內(nèi)通用。在以下各章展開的概念是那些應(yīng)用領(lǐng)域所用方法的基礎(chǔ)。
1.2 數(shù)字圖像處理的起源
數(shù)字圖像處理最早的應(yīng)用之一是在報紙業(yè)，當(dāng)時，圖像第一次通過海底電纜從倫敦傳往紐約。早在20世紀20年代曾引入 Btutlane 電纜圖片傳輸系統(tǒng)，把橫跨大西洋傳送一幅圖片所需的時間從一個多星期減少到3個小時。為了用電纜傳輸圖片，首先要進行編碼，然后在接收端用特殊的打印設(shè)備重構(gòu)該圖片。圖 1.1就是用這種方法傳送并利用電報打印機通過字符模擬中間色調(diào)還原出來的圖像。這些早期數(shù)字圖像視覺質(zhì)量的改進工作，涉及到打印過程的選擇和亮度等級的分布等問題。用于得到圖1.1的打印方法到1921年底就被徹底淘汰了，轉(zhuǎn)而支持一種基于光學(xué)還原的技術(shù)，該技術(shù)在電報接收端用穿孔紙帶打出圖片。圖1.2 就是用這種方法得到的圖像，對比圖1.1，它在色調(diào)質(zhì)量和分辨率方面的改進都很明顯。
圖1.2 1922年在信號兩次穿越大西洋后，
從穿孔紙帶得到的數(shù)字圖像，可以
看出某些差錯( McFalsne)
圖1.1 1421年由電報打印機采用特殊字
符在編碼紙帶中產(chǎn)生的數(shù)字圖像
( McFalsne)
早期的Bartlane系統(tǒng)可以用5個灰度等級對圖像編碼，到1929年已增加到15個等級。圖1.3所示的這種典型類型的圖像就是用15級色調(diào)設(shè)備得到的。在這一時期，由于引入了一種用編碼圖像紙帶去調(diào)制光束而使底片感光的系統(tǒng)，明顯地改善了復(fù)原過程。剛才引用的數(shù)字圖像的例子并沒有考慮數(shù)字圖像處理的結(jié)果，這主要是因為沒有涉及到計算機。因此，數(shù)字圖像處理的歷史與數(shù)字計算機的發(fā)展密切相關(guān)。事實上，數(shù)字圖像要求非常大的存儲和計算能力，因此數(shù)字圖像處理領(lǐng)域的發(fā)展必須依靠數(shù)字計算機及數(shù)據(jù)存儲、顯示和傳輸?shù)认嚓P(guān)技術(shù)的發(fā)展。計算機的概念可追溯到5000多年前中國算盤的發(fā)明。近兩個世紀以來的一些發(fā)展也奠定了計算機的基礎(chǔ)。然而，現(xiàn)代計算機的基礎(chǔ)還要回溯到20世紀40年代由約翰·馮·諾依曼提出的兩個重要概念:(l)保存程序和數(shù)據(jù)的存儲器;(2)條件分支。這兩個概念是中央處理單元(CPU)的基礎(chǔ)。今天，它是計算機的心臟。從馮·諾依曼開始，引發(fā)了一系列重要技術(shù)進步，使得計算機以強大的功能用于數(shù)字圖像處理領(lǐng)域。簡單說，這些進步可歸納為如下幾點:
(1)1948年貝爾實驗室發(fā)明了晶體三極管
(2)20世紀50年代到20世紀60年代高級編程語言(如COBOL和FORTRAN)的
開發(fā)；
(3)1958年得州儀器公司發(fā)明了集成電路(IC);
(4)20世紀60年代早期操作系統(tǒng)的發(fā)展;
(5)20世紀70年代 Intel 公司開發(fā)了微處理器(由中央處理單元、存儲器和輸
入、輸出控制組成的單一芯片);
(6)1981年IBM公司推出了個人計算機;
(7)20世紀70年代出現(xiàn)的大規(guī)模集成電路(LI)所引發(fā)的元件微小化革命，20世
紀80年代出現(xiàn)了YLSI(超大規(guī)模集成電路)，現(xiàn)在已出現(xiàn)了ULSI。
圖1. 3在1929年從倫敦到紐約用15級色調(diào)設(shè)備通過電纜
傳送的 Cenerale Pershing和 Foch的未經(jīng)修飾的照片
伴隨著這些技術(shù)進步，大規(guī)模的存儲和顯示系統(tǒng)也隨之發(fā)展起來。這兩者均是數(shù)字圖像處理的基礎(chǔ)。第一臺可以執(zhí)行有意義的圖像處理任務(wù)的大型計算機出現(xiàn)在20世紀60年代早期。數(shù)字圖像處理技術(shù)的誕生可追溯至這一時期這些機器的使用和空間項目的開發(fā)，這兩大發(fā)展把人們的注意力集中到數(shù)字圖像處理的潛能上。利用計算機技術(shù)改善空間探測器發(fā)回的圖像的工作，始于 1964 年美國加利福尼亞的噴氣推進實驗室。當(dāng)時由“旅行者 7 號”衛(wèi)星傳送的月球圖像由一臺計算機進行了處理，以校正航天器上電視攝像機中各種類型的圖像畸變。圖1.4顯示了由“旅行者7號”于1954年7月31日上午(東部白天時間)9點09分在光線影響月球表面前約17分鐘時攝取的第一張月球圖像痕跡(稱為網(wǎng)狀痕跡)用于幾何校正，在第5章將討論該間題]，這也是美國航天器取得的第一幅月球圖像?！奥眯姓?7 號”傳送的圖像可作為改善的增強和復(fù)原圖像(例如來自“探索者”登月一飛行、“水手號”系列空間探淵器及阿波羅載人登月飛行的圖像)方法的基礎(chǔ)。進行空間應(yīng)用的同時，數(shù)字圖像處理技術(shù)在20世紀60年代末和20世紀70年代初開始用于醫(yī)學(xué)圖像、地球遙感監(jiān)測和天文學(xué)等領(lǐng)域。早在20世紀70年代發(fā)明的計算機軸向斷層術(shù)(CAT)[簡稱計算機斷層(CT)]是圖像處理在醫(yī)學(xué)診斷領(lǐng)域最重要的應(yīng)用之一。計算機軸向斷層術(shù)是一種處理方法，在這種處理中，一個檢測器環(huán)圍繞著一個物體(或病人)，并且一個 x 射線源(與檢測器環(huán)同心)繞著物體旋轉(zhuǎn)。X 射線穿過物體并由位于對面環(huán)中的相應(yīng)檢測器收集起來。當(dāng) X 射線源旋轉(zhuǎn)時，重復(fù)這一過程。斷層技術(shù)由一些算法組成，該算法用感知的數(shù)據(jù)去重建通過物體的“切片”圖像。當(dāng)物體沿垂直于檢測器的方向運動時就產(chǎn)生一系列這樣的“切片”，這些切片組成了物體內(nèi)部的再現(xiàn)圖像。斷層技術(shù)是由 Godfrey N.Hounsfield 先生和 Allan M.Cormack 教授發(fā)明的，他們共同獲得了 1979 年諾貝爾醫(yī)學(xué)獎。X 射線是在 1895 年由威廉·康拉德·倫琴發(fā)現(xiàn)的，由于這一發(fā)現(xiàn)，他獲得了 I901 年諾貝爾物理學(xué)獎。這兩項發(fā)明相差近 100 年。它們在今天引領(lǐng)著圖像處理某些最活躍的應(yīng)用領(lǐng)域。
圖1. 4美國航天器傳送的第一張月球照片，“旅行者7號”
衛(wèi)星1964年7月31日9點 09分(東部白天時間)在
光線影響月球表面前17分鐘時攝取的圖像
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