數(shù)字圖像處理作業(yè)

1、數(shù)字圖像處理課程論文姓名：學號：一、 直方圖原理分析圖像增強是指按特定的需要突出一幅圖像中的某些信息，同時，消弱或去除某些不需要的信息的處理方法。其主要目的是處理后的圖像對某些特定的應用比原來的圖像更加有效。圖像增強技術主要有直方圖修改處理、圖像平滑化處理、圖像尖銳化處理和彩色處理技術等。 直方圖是多種空間城處理技術的基礎。直方圖操作能有效地用于圖像增強。除了提供有用的圖像統(tǒng)計資料外，直方圖固有的信息在其他圖像處理應用中也是非常有用的，如圖像壓縮與分割。直方圖在軟件中易于計算，也適用于商用硬件設備，因此，它們成為了實時圖像處理的一個流行工具。 直方圖是圖像的最基本的統(tǒng)計特征，它反映的是圖像的灰

2、度值的分布情況。直方圖均衡化的目的是使圖像在整個灰度值動態(tài)變化范圍內(nèi)的分布均勻化，改善圖像的亮度分布狀態(tài)，增強圖像的視覺效果?；叶戎狈綀D是圖像預處理中涉及最廣泛的基本概念之一。圖像的直方圖事實上就是圖像的亮度分布的概率密度函數(shù)，是一幅圖像的所有象素集合的最基本的統(tǒng)計規(guī)律。直方圖反映了圖像的明暗分布規(guī)律，可以通過圖像變換進行直方圖調(diào)整，獲得較好的視覺效果。直方圖均衡化是通過灰度變換將一幅圖像轉換為另一幅具有均衡直方圖，即在每個灰度級上都具有相同的象素點數(shù)的過程。 處理后的圖像直方圖分布更均勻了，圖像在每個灰度級上都有像素點。從處理前后的圖像可以看出，許多在原始圖像中看不清楚的細節(jié)在直方圖均衡化處

3、理后所得到的圖像中都變得十分清晰。(1)直方圖均衡化原理直方圖均衡化又稱直方圖平坦化,是將一已知灰度概率密度分布的圖像經(jīng)過某種變換,變成一幅具有均勻灰度概率密度分布的新圖像.其結果是擴展了像元取值的動態(tài)范圍,從而達到增強圖像整體對比度的效果。直方圖均衡化是圖像處理領域中利用圖像直方圖對對比度進行調(diào)整的方法。這種方法通常用來增加許多圖像的局部對比度，尤其是當圖像的有用數(shù)據(jù)的對比度相當接近的時候。通過這種方法，亮度可以更好地在直方圖上分布。這樣就可以用于增強局部的對比度而不影響整體的對比度，直方圖均衡化通過有效地擴展常用的亮度來實現(xiàn)這種功能。直方圖均衡化的具體實現(xiàn)步驟如下:（1）1）.列出原始圖像

4、的灰度級2)統(tǒng)計各灰度級的像素數(shù)目3).計算原始圖像直方圖各灰度級的頻數(shù)4).計算累積分布函數(shù)5).應用以下公式計算映射后的輸出圖像的灰度級，P為輸出圖像灰度級的個數(shù)，其中INT為取整符號6).統(tǒng)計映射后各灰度級的像素數(shù)目7).計算輸出直方圖8).用fj和gi的映射關系修改原始圖像的灰度級,從而獲得直方圖近似為均勻分布的輸出圖像(2)直方圖規(guī)定化原理直方圖均衡化的優(yōu)點是能自動增強整個圖像的對比度,但它的具體增強效果不易控制,處理的結果總是得到全局的均衡化的直方圖.實際工作中,有時需要變換直方圖使之成為某個特定的形狀,從而有選擇地增強某個灰度值范圍內(nèi)的對比度,這時可采用比較靈活的直方圖規(guī)定化方法

5、。所謂直方圖規(guī)定化，就是通過一個灰度映像函數(shù)，將原灰度直方圖改造成所希望的直方圖。所以，直方圖修正的關鍵就是灰度映像函數(shù)。直方圖規(guī)定化增強處理的步驟如下:令Pr（r）和Pz（z）分別為原始圖像和期望圖像的灰度概率密度函數(shù)。如果對原始圖像和期望圖像均作直方圖均衡化處理，應有由于都是進行均衡化處理，處理后的原圖像概率密度函數(shù)Ps（S）及理想圖像概率密度函數(shù)PV（V）是相等的。于是，我們可以用變換后的原始圖像灰度級S代替（2）式中的V。即這時的灰度級Z便是所希望的圖像的灰度級。此外，利用（1）與（3）式還可得到組合變換函數(shù)對連續(xù)圖像，重要的是給出逆變換解析式。對離散圖像而言，有二、 基于MATLAB

6、的直方圖增強技術編程程序：clc;clear;H=imread(001.jpg); %讀入原圖像 subplot(221),imshow(H); %顯示原圖像title(原圖像) I=rgb2gray(H); %將原圖像轉換為灰度圖像subplot(223),imshow(I); title(灰度圖像)subplot(224),imhist(I);title(灰度圖像直方圖)figure(2)J=histeq(I); %對灰度圖像進行直方圖均衡化處理 subplot(221),imshow(J); title(均衡化圖像)subplot(222),imhist(J);title(均衡化圖像直方

7、圖) subplot(223),imhist(I,64); %將原圖像直方圖顯示為 64 級灰度 title(灰度64圖像直方圖) subplot(224),imhist(J,64); %將均衡化后圖像的直方圖顯示為 64 級灰度 title(灰度64均衡化圖像直方圖) figure(3)hgram=50:2:250;K=histeq(I,hgram); subplot(221),imshow(K) ;title(規(guī)定化圖像);subplot(222),imhist(K,256); title(規(guī)定化圖像直方圖)運行圖像：三、 結果與分析從上圖中可以看出，用直方圖均衡化后，圖像的直方圖的灰度間

8、隔被拉大了，均衡化的圖像的一些細節(jié)顯示了出來，這有利于圖像的分析和識別。直方圖均衡化就是通過變換函數(shù)histeq將原圖的直方圖調(diào)整為具有“平坦”傾向的直方圖，然后用均衡直方圖校正圖像。直方圖均衡化對于背景和前景都太亮或者太暗的圖像非常有用，這種方法尤其是可以帶來X光圖像中更好的骨骼結構顯示以及曝光過度或者曝光不足照片中更好的細節(jié)。這種方法的一個主要優(yōu)勢是它是一個相當直觀的技術并且是可逆操作，如果已知均衡化函數(shù)，那么就可以恢復原始的直方圖，并且計算量也不大。直方圖均衡化的一個缺點是它對處理的數(shù)據(jù)不加選擇，它可能會增加背景雜訊的對比度并且降低有用信號的對比度；變換后圖像的灰度級減少，某些細節(jié)消失；

9、某些圖像，如直方圖有高峰，經(jīng)處理后對比度不自然的過分增強。直方圖均衡化能夠自動增強整個圖像的對比度，但它的具體增強效果不容易控制，處理的結果總是得到全局均勻化的直方圖，一般來說正確地選擇規(guī)定化的函數(shù)可以獲得比直方圖均衡化更好的效果。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用領域: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x，y和幅值f為有限的、離散

10、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用領域。圖像處理涉及的范疇或其他相關領域(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

11、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。人工智能領域處在其發(fā)展過程中的初期階段，它的發(fā)展比預期的要慢得多，圖像分析(也稱為圖像理解)領域則處在圖像處理和計算機視覺兩個學科之間。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

12、、中級和高級處理)來區(qū)分其中的各個學科。低級處理涉及初級操作，如降低噪聲的圖像預處理，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域。這樣，在本書

13、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)理解的復雜度從圖像分析或計算機視覺領域考慮問題。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)濟價值的領域內(nèi)通用。在以下各章展開的概念是那些應用領域所用方法的基礎。1.2數(shù)字圖像處理的起源數(shù)字圖像處理最早的應用之一是在報紙業(yè)

14、，當時，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備重構該圖片。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，轉而支持一種基于光學還原的技術，該技術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)質量和分辨率方面

15、的改進都很明顯。 圖1.1 1421年由電報打印機采用特殊字 圖1.2 1922年在信號兩次穿越大西洋后， 符在編碼紙帶中產(chǎn)生的數(shù)字圖像 從穿孔紙帶得到的數(shù)字圖像，可以 ( McFalsne) 看出某些差錯 ( 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

16、切相關。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技術的發(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。然而，現(xiàn)代計算機的基礎還要回溯到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。今天，它是計算機的心臟。從馮諾依曼開始，引發(fā)了一系列重要技術進步，使得計算機以強大的功能用于數(shù)字圖像處理領域。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)

17、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)設備通過電纜傳送的Cenerale Pershing和Foch的未經(jīng)

18、修飾的照片伴隨著這些技術進步，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。第一臺可以執(zhí)行有意義的圖像處理任務的大型計算機出現(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1

19、7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?號”傳送的圖像可作為改善的增強和復原圖像(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。進行空間應用的同時，數(shù)字圖像處理技術在20世紀60年代末和20世紀70年代初開始用于醫(yī)學圖像、地球遙感監(jiān)測和天文學等領域。早在20世紀70年代發(fā)明的計算機軸向斷層術(CAT)簡稱計算機斷層(CT)是圖像處理在醫(yī)學診斷領域最重要的應用之一。計算機軸向斷層術是一種處理方法，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

20、射線源(與檢測器環(huán)同心)繞著物體旋轉。X射線穿過物體并由位于對面環(huán)中的相應檢測器收集起來。當X射線源旋轉時，重復這一過程。斷層技術由一些算法組成，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年諾貝爾物理學獎。這兩項發(fā)明相差近100年。它們在今天引領著圖像處理某些最活躍的應用領域。圖1.4

21、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The research of digital image processing technique 1 IntroductionInterest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing

22、of image data for storage, transmission, and representation for autonomous machine perception. This chapter has several objectives: (1)to define the scope of the 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

23、the art in image processing by examining some of the principal 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 typical, general-purpose image processing system; and (6) to provide di

24、rection to the books and other literature where image processing work normally is reporter.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 c

25、alled 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 l

26、ocation 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. We consider these definitions in more formal terms in Chapter2. Vision is the most advanced of our senses, so it is n

27、ot 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 genera

28、ted 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 p

29、rocessing 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

30、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 hum

31、an 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

32、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 are no clear-cut boundaries in the continuum from image processing at one end to computer vision at the other. However

33、, 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 character

34、ized 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

35、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

36、 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 indivi

37、dual 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

38、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 proces

39、sing, 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 state

40、ment “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

41、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 NewYork. Introduction of the Bartlane cable picture transmission system in the early 1920s reduced the

42、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

43、 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

44、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 1.1 A d

45、igital picture produced in FIGURE 1.2 A digital picture1921 from a coded tape by a telegraph printer made in 1922 from a tape punchedWith special type faces (McFarlane) after the signals had crossed the Atlantic twice. Some errors are Visible. (McFarlane)The early Bartlane systems were capable of co

46、ding 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

47、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 proc

48、essing 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

49、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 m

50、odern 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 t

51、oday. 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 1

52、960s 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

53、 (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 ver

54、y 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 Pershingthat New York delivers with 15 level tone equipmentsthrough cable with Foch do not the photograph by decorationConcurrent with these advances w

55、ere 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 processin

56、g 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 s

57、pace 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 (th