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(xué)號(hào)：一、 直方圖原理分析圖像增強(qiáng)是指按特定的需要突出一幅圖像中的某些信息，同時(shí)，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(duì)某些特定的應(yīng)用比原來(lái)的圖像更加有效。圖像增強(qiáng)技術(shù)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(shù)等。  直方圖是多種空間城處理技術(shù)的基礎(chǔ)。直方圖操作能有效地用于圖像增強(qiáng)。除了提供有用的圖像統(tǒng)計(jì)資料外，直方圖固有的信息在其他圖像處理應(yīng)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(jì)算，也適用于商用硬件設(shè)備，因此，它們成為了實(shí)時(shí)圖像處理的一個(gè)流行工具。  直方圖是圖像的最基本的統(tǒng)計(jì)

2、特征，它反映的是圖像的灰度值的分布情況。直方圖均衡化的目的是使圖像在整個(gè)灰度值動(dòng)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(qiáng)圖像的視覺(jué)效果?；叶戎狈綀D是圖像預(yù)處理中涉及最廣泛的基本概念之一。 圖像的直方圖事實(shí)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(jì)規(guī)律。直方圖反映了圖像的明暗分布規(guī)律，可以通過(guò)圖像變換進(jìn)行直方圖調(diào)整，獲得較好的視覺(jué)效果。直方圖均衡化是通過(guò)灰度變換將一幅圖像轉(zhuǎn)換為另一幅具有均衡直方圖，即在每個(gè)灰度級(jí)上都具有相同的象素點(diǎn)數(shù)的過(guò)程。  處理后的圖像直方圖分布更均勻了，圖像在每個(gè)灰度級(jí)上都有像素點(diǎn)。從處理前后的圖像可以看出

3、，許多在原始圖像中看不清楚的細(xì)節(jié)在直方圖均衡化處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(chēng)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(guò)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(jié)果是擴(kuò)展了像元取值的動(dòng)態(tài)范圍,從而達(dá)到增強(qiáng)圖像整體對(duì)比度的效果。直方圖均衡化是圖像處理領(lǐng)域中利用圖像直方圖對(duì)對(duì)比度進(jìn)行調(diào)整的方法。這種方法通常用來(lái)增加許多圖像的局部對(duì)比度，尤其是當(dāng)圖像的有用數(shù)據(jù)的對(duì)比度相當(dāng)接近的時(shí)候。通過(guò)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(qiáng)局部的對(duì)比度而不影響整體的對(duì)比度，直方圖均衡化通過(guò)有效地?cái)U(kuò)展常用的亮度

4、來(lái)實(shí)現(xiàn)這種功能。直方圖均衡化的具體實(shí)現(xiàn)步驟如下: （1） 1）.列出原始圖像的灰度級(jí)2)統(tǒng)計(jì)各灰度級(jí)的像素?cái)?shù)目3).計(jì)算原始圖像直方圖各灰度級(jí)的頻數(shù)4).計(jì)算累積分布函數(shù)5).應(yīng)用以下公式計(jì)算映射后的輸出圖像的灰度級(jí)，P為輸出圖像灰度級(jí)的個(gè)數(shù)，其中INT為取整符號(hào)6).統(tǒng)計(jì)映射后各灰度級(jí)的像素?cái)?shù)目7). 計(jì)算輸出直方圖8). 用fj和gi的映射關(guān)系修改原始圖像的灰度級(jí),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(diǎn)是能自動(dòng)增強(qiáng)整個(gè)圖像的對(duì)比度,但它的具體增強(qiáng)效果不易控制,處理的結(jié)果總是得到全局的均衡化的直方圖.

5、實(shí)際工作中,有時(shí)需要變換直方圖使之成為某個(gè)特定的形狀,從而有選擇地增強(qiáng)某個(gè)灰度值范圍內(nèi)的對(duì)比度,這時(shí)可采用比較靈活的直方圖規(guī)定化方法。所謂直方圖規(guī)定化，就是通過(guò)一個(gè)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(guān)鍵就是灰度映像函數(shù)。直方圖規(guī)定化增強(qiáng)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(duì)原始圖像和期望圖像均作直方圖均衡化處理，應(yīng)有由于都是進(jìn)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(jí)S代替（2）式中的V。即這時(shí)的灰度級(jí)Z 便是所

6、希望的圖像的灰度級(jí)。 此外，利用（1）與（3）式還可得到組合變換函數(shù)對(duì)連續(xù)圖像，重要的是給出逆變換解析式。對(duì)離散圖像而言，有二、 基于MATLAB的直方圖增強(qiáng)技術(shù)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(zhuǎn)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

7、圖')figure(2)J=histeq(I); %對(duì)灰度圖像進(jìn)行直方圖均衡化處理 subplot(221),imshow(J); title('均衡化圖像')subplot(222),imhist(J);title('均衡化圖像直方圖') subplot(223),imhist(I,64); %將原圖像直方圖顯示為 64 級(jí)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(jí)灰度 title('灰度64均衡化圖像直方圖') figure

8、(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(yùn)行圖像：三、 結(jié)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隔被拉大了，均衡化的圖像的一些細(xì)節(jié)顯示了出來(lái)，這有利于圖像的分析和識(shí)別。直方圖均衡化就是通過(guò)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(duì)于背景和前景都太亮或者太暗的圖像非常有用，這種

9、方法尤其是可以帶來(lái)X光圖像中更好的骨骼結(jié)構(gòu)顯示以及曝光過(guò)度或者曝光不足照片中更好的細(xì)節(jié)。這種方法的一個(gè)主要優(yōu)勢(shì)是它是一個(gè)相當(dāng)直觀的技術(shù)并且是可逆操作，如果已知均衡化函數(shù)，那么就可以恢復(fù)原始的直方圖，并且計(jì)算量也不大。直方圖均衡化的一個(gè)缺點(diǎn)是它對(duì)處理的數(shù)據(jù)不加選擇，它可能會(huì)增加背景雜訊的對(duì)比度并且降低有用信號(hào)的對(duì)比度；變換后圖像的灰度級(jí)減少，某些細(xì)節(jié)消失；某些圖像，如直方圖有高峰，經(jīng)處理后對(duì)比度不自然的過(guò)分增強(qiáng)。直方圖均衡化能夠自動(dòng)增強(qiáng)整個(gè)圖像的對(duì)比度，但它的具體增強(qiáng)效果不容易控制，處理的結(jié)果總是得到全局均勻化的直方圖，一般來(lái)說(shuō)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

10、方法的研究1 緒論數(shù)字圖像處理方法的研究源于兩個(gè)主要應(yīng)用領(lǐng)域:其一是為了便于人們分析而對(duì)圖像信息進(jìn)行改進(jìn)；其二是為了使機(jī)器自動(dòng)理解而對(duì)圖像數(shù)據(jù)進(jìn)行存儲(chǔ)、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(gè)二維函數(shù)f(x, y)，這里x和y是空間坐標(biāo)，而在任何一對(duì)空間坐標(biāo)f(x, y)上的幅值f稱(chēng)為該點(diǎn)圖像的強(qiáng)度或灰度。當(dāng)x，y和幅值f為有限的、離散的數(shù)值時(shí)，稱(chēng)該點(diǎn)是由有限的元素組成的，沒(méi)一個(gè)元素都有一個(gè)特定的位置和幅值，這些元素稱(chēng)為圖像元素、畫(huà)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(yǔ)研究這些定義。視覺(jué)是人類(lèi)最高級(jí)的感知器官，所以，毫無(wú)疑問(wèn)圖像在人類(lèi)感

11、知中扮演著最重要的角色。然而，人類(lèi)感知只限于電磁波譜的視覺(jué)波段，成像機(jī)器則可覆蓋幾乎全部電磁波譜，從伽馬射線(xiàn)到無(wú)線(xiàn)電波。它們可以對(duì)非人類(lèi)習(xí)慣的那些圖像源進(jìn)行加工，這些圖像源包括超聲波、電子顯微鏡及計(jì)算機(jī)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(jì)算機(jī)視覺(jué))的界定在初創(chuàng)人之間并沒(méi)有一致的看法。有時(shí)用處理的輸人和輸出內(nèi)容都是圖像這一特點(diǎn)來(lái)界定圖像處理的范圍。我們認(rèn)為這一定義僅是人為界定和限制。例如，在這個(gè)定義下，甚至最普通的計(jì)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(jì)算機(jī)視覺(jué))研究的最高目標(biāo)是用計(jì)算機(jī)去模擬

12、人類(lèi)視覺(jué)，包括理解和推理并根據(jù)視覺(jué)輸人采取行動(dòng)等。這一領(lǐng)域本身是人工智能的分支，其目的是模仿人類(lèi)智能。人工智能領(lǐng)域處在其發(fā)展過(guò)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(chēng)為圖像理解)領(lǐng)域則處在圖像處理和計(jì)算機(jī)視覺(jué)兩個(gè)學(xué)科之間。從圖像處理到計(jì)算機(jī)視覺(jué)這個(gè)連續(xù)的統(tǒng)一體內(nèi)并沒(méi)有明確的界線(xiàn)。然而，在這個(gè)連續(xù)的統(tǒng)一體中可以考慮三種典型的計(jì)算處理(即低級(jí)、中級(jí)和高級(jí)處理)來(lái)區(qū)分其中的各個(gè)學(xué)科。低級(jí)處理涉及初級(jí)操作，如降低噪聲的圖像預(yù)處理，對(duì)比度增強(qiáng)和圖像尖銳化。低級(jí)處理是以輸人、輸出都是圖像為特點(diǎn)的處理。中級(jí)處理涉及分割 把圖像分為不同區(qū)域或目標(biāo)物)以及縮減對(duì)目標(biāo)物的描述，以使其更適合計(jì)算機(jī)處

13、理及對(duì)不同日標(biāo)的分類(lèi)(識(shí)別)。中級(jí)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(biāo)識(shí)等)為特點(diǎn)的。最后，高級(jí)處理涉及在圖像分析中被識(shí)別物體的總體理解，以及執(zhí)行與視覺(jué)相關(guān)的識(shí)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(gè)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(shí)別這一領(lǐng)域。這樣，在本書(shū)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(shí)也包括從圖像中提取特征及識(shí)別特定物體的處理。舉一個(gè)簡(jiǎn)單的文本自動(dòng)分析方面的例子來(lái)具體說(shuō)明這一概念。在自動(dòng)分析文本時(shí)首先獲取一幅包含文本的圖像，對(duì)該圖像進(jìn)行預(yù)處理，提取(分割)字符，然后以

14、適合計(jì)算機(jī)處理的形式描述這些字符，最后識(shí)別這些字符，而所有這些操作都在本書(shū)界定的數(shù)字圖像處理的范圍內(nèi)。理解一頁(yè)的內(nèi)容可能要根據(jù)理解的復(fù)雜度從圖像分析或計(jì)算機(jī)視覺(jué)領(lǐng)域考慮問(wèn)題。這樣，本書(shū)定義的數(shù)字圖像處理的概念將在有特殊社會(huì)和經(jīng)濟(jì)價(jià)值的領(lǐng)域內(nèi)通用。在以下各章展開(kāi)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(bào)紙業(yè)，當(dāng)時(shí)，圖像第一次通過(guò)海底電纜從倫敦傳往紐約。早在20世紀(jì)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(shí)間從一個(gè)多星期減少到3個(gè)小時(shí)。為了用電纜傳輸圖片，首先要進(jìn)行編碼，然后在接收端用特殊的打印設(shè)備重構(gòu)該圖片。

15、圖1.1就是用這種方法傳送并利用電報(bào)打印機(jī)通過(guò)字符模擬中間色調(diào)還原出來(lái)的圖像。這些早期數(shù)字圖像視覺(jué)質(zhì)量的改進(jìn)工作，涉及到打印過(guò)程的選擇和亮度等級(jí)的分布等問(wèn)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(bào)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(duì)比圖1.1，它在色調(diào)質(zhì)量和分辨率方面的改進(jìn)都很明顯。 圖1.1 1421年由電報(bào)打印機(jī)采用特殊字 圖1.2 1922年在信號(hào)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(cuò) ( McFalsne) 早期的Bar

16、tlane系統(tǒng)可以用5個(gè)灰度等級(jí)對(duì)圖像編碼，到1929年已增加到15個(gè)等級(jí)。圖1.3所示的這種典型類(lèi)型的圖像就是用15級(jí)色調(diào)設(shè)備得到的。在這一時(shí)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(guò)程。剛才引用的數(shù)字圖像的例子并沒(méi)有考慮數(shù)字圖像處理的結(jié)果，這主要是因?yàn)闆](méi)有涉及到計(jì)算機(jī)。因此，數(shù)字圖像處理的歷史與數(shù)字計(jì)算機(jī)的發(fā)展密切相關(guān)。事實(shí)上，數(shù)字圖像要求非常大的存儲(chǔ)和計(jì)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(jì)算機(jī)及數(shù)據(jù)存儲(chǔ)、顯示和傳輸?shù)认嚓P(guān)技術(shù)的發(fā)展。計(jì)算機(jī)的概念可追溯到5000多年前中國(guó)算盤(pán)的發(fā)明。近兩個(gè)世紀(jì)以來(lái)的一些發(fā)展也奠定了計(jì)算機(jī)的基礎(chǔ)。然而，現(xiàn)代計(jì)

17、算機(jī)的基礎(chǔ)還要回溯到20世紀(jì)40年代由約翰·馮·諾依曼提出的兩個(gè)重要概念:(l)保存程序和數(shù)據(jù)的存儲(chǔ)器;(2)條件分支。這兩個(gè)概念是中央處理單元(CPU)的基礎(chǔ)。今天，它是計(jì)算機(jī)的心臟。從馮·諾依曼開(kāi)始，引發(fā)了一系列重要技術(shù)進(jìn)步，使得計(jì)算機(jī)以強(qiáng)大的功能用于數(shù)字圖像處理領(lǐng)域。簡(jiǎn)單說(shuō)，這些進(jìn)步可歸納為如下幾點(diǎn):(1) 1948年貝爾實(shí)驗(yàn)室發(fā)明了晶體三極管；(2) 20世紀(jì)50年代到20世紀(jì)60年代高級(jí)編程語(yǔ)言(如COBOL和FORTRAN)的開(kāi)發(fā)；(3) 1958年得州儀器公司發(fā)明了集成電路(IC);(4) 20世紀(jì)60年代早期操作系統(tǒng)的發(fā)展;(5) 20世紀(jì)70年代

18、Intel公司開(kāi)發(fā)了微處理器(由中央處理單元、存儲(chǔ)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(gè)人計(jì)算機(jī);(7) 20世紀(jì)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(jì)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(jí)色調(diào)設(shè)備通過(guò)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(jìn)步，大規(guī)模的存儲(chǔ)和顯示系統(tǒng)也隨之發(fā)展起來(lái)。這兩者均是數(shù)字圖像處理的基礎(chǔ)。第一臺(tái)可以執(zhí)行有意義的圖像處理任務(wù)的大型計(jì)算機(jī)出現(xiàn)在20世紀(jì)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(shí)

19、期這些機(jī)器的使用和空間項(xiàng)目的開(kāi)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(jì)算機(jī)技術(shù)改善空間探測(cè)器發(fā)回的圖像的工作，始于1964年美國(guó)加利福尼亞的噴氣推進(jìn)實(shí)驗(yàn)室。當(dāng)時(shí)由“旅行者7號(hào)”衛(wèi)星傳送的月球圖像由一臺(tái)計(jì)算機(jī)進(jìn)行了處理，以校正航天器上電視攝像機(jī)中各種類(lèi)型的圖像畸變。圖1.4顯示了由“旅行者7號(hào)”于1954年7月31日上午(東部白天時(shí)間)9點(diǎn)09分在光線(xiàn)影響月球表面前約17分鐘時(shí)攝取的第一張?jiān)虑驁D像痕跡(稱(chēng)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(guó)航天器取得的第一幅月球圖像?！奥眯姓?號(hào)”傳送的圖像可作為改善的增強(qiáng)和復(fù)原圖像(例如來(lái)自“探索者”登月一飛行、“水

20、手號(hào)”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(jìn)行空間應(yīng)用的同時(shí)，數(shù)字圖像處理技術(shù)在20世紀(jì)60年代末和20世紀(jì)70年代初開(kāi)始用于醫(yī)學(xué)圖像、地球遙感監(jiān)測(cè)和天文學(xué)等領(lǐng)域。早在20世紀(jì)70年代發(fā)明的計(jì)算機(jī)軸向斷層術(shù)(CAT)簡(jiǎn)稱(chēng)計(jì)算機(jī)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(jì)算機(jī)軸向斷層術(shù)是一種處理方法，在這種處理中，一個(gè)檢測(cè)器環(huán)圍繞著一個(gè)物體(或病人)，并且一個(gè)x射線(xiàn)源(與檢測(cè)器環(huán)同心)繞著物體旋轉(zhuǎn)。X射線(xiàn)穿過(guò)物體并由位于對(duì)面環(huán)中的相應(yīng)檢測(cè)器收集起來(lái)。當(dāng)X射線(xiàn)源旋轉(zhuǎn)時(shí)，重復(fù)這一過(guò)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(guò)物體的“切片”圖像。當(dāng)物體沿垂直于

21、檢測(cè)器的方向運(yùn)動(dòng)時(shí)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(jiǎng)。X射線(xiàn)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(jiǎng)。這兩項(xiàng)發(fā)明相差近100年。它們?cè)诮裉煲I(lǐng)著圖像處理某些最活躍的應(yīng)用領(lǐng)域。圖1.4美國(guó)航天器傳送的第一張?jiān)虑蛘掌?，“旅行?號(hào)”衛(wèi)星1964年7月31日9點(diǎn)09分(東部白天時(shí)間)在光線(xiàn)影響月球表面前17分鐘時(shí)攝取的圖像The research of digital

22、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 of image data for storage, transmission, and representation for autonomous machine perceptio

23、n. 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 the art in image processing by examining some of the principal area in which it is applied;

24、(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 direction to the books and other literature where image processing work normally is reporter.1

25、.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.

26、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,

27、 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,

28、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 ultras

29、ound, 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.

30、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

31、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 vi

32、sual 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 (al

33、so 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 , one useful paradigm is to consider three types of computerized processes is this continuum

34、: 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 i

35、nvolves 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

36、 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

37、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 processin

38、g 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

39、 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 i

40、mage 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 de

41、fined 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 i

42、mages 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 time required to transport a picture across the Atlantic from more than a week to less than

43、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 improv

44、ing 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

45、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 digital picture produced in FIGURE 1.2 A digital picture1921 from a coded tape by a telegraph

46、 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 coding images in five distinct level of gray. This capability was increased to 15 levels in 19

47、29. 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

48、 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

49、 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

50、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 Neuman

51、n 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 power

52、ful 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 F

53、ORTRAN ( 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 cont

54、rols) 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 integ

55、ration (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 were development in the areas of mass storage and display systems, both of which are fundamen

56、tal 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 progr

57、am 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 pictur

58、es 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