數(shù)字圖像增強(qiáng)外文文獻(xiàn)附翻譯

DigitalImageProcessingandEdgeDetectionDigitalImageProcessingInterestindigitalimageprocessingmethodsstemsfromtwoprincipalapplica-tionareas:improvementofpictorialinformationforhumaninterpretation;andprocessingofimagedataforstorag,transmission,andrepresentationforau-se.Animagemaybedefinedasatwo-dimensionalfunction,f(x,y),wherexandyarespatial(plane)coordinate,andtheamplitudeoffatanypairofcoordinates(x,y)iscalledtheintensityorgryleveloftheimageatthatpoint.henx,y,andtheamplitudevaluesoffareallfinit,discretequantitie,wecalltheimageadigitalimage.hefieldofdigitalimageprocessingreferstoprocessingdigitalimagesbymeansofadigitalcompute.Notethatadigitalimageiscomdfaerf,hfhsarnvalu.heseelementsarereferredtoaspictureelements,imageelements,pels,andpiels.Pielisthetermmostwidelyusedtodenotetheelementsofadigitalimag.Visionisthemostadvancedofoursenses,soitisnotsurprisingthatimagesplaythesinglemostimportantroleinhumanperception.However,unlikehumans,whoarelimitedtothevisualbandoftheelectromagnetic(EM)spec-trum,imagingmachinescoveralmosttheentireEMspectrum,rangingfromgammatoradiowaves.Theycanoperateonimagesgeneratedbysourcesthathumansarenotaccustomedtoassociatingwithimages.Theseincludeultra-sound,electronmicroscopy,andcomputer-generatedimages.Thus,digitalimageprocessingencompassesawideandvariedfieldofapplications.Thereisnogeneralagreementamongauthorsregardingwhereimageprocessingstopsandotherrelatedareas,suchasimageanalysisandcomputervi-sion,start.Sometimesadistinctionismadebydefiningimageprocessingasadisciplineinwhichboththeinputandoutputofaprocessareimages.Webelievethistobealimitingandsomewhatartificialboundary.Forexample,underthisdefinition,eventhetrivialtaskofcomputingtheaverageintensityofanimage(whichyieldsasinglenumber)wouldnotbeconsideredanimageprocessingoperation.Ontheotherhand,therearefieldssuchascomputervisionwhoseultimategoalistousecomputerstoemulatehumanvision,includinglearningandbeingabletomakeinferencesandtakeactionsbasedonvisualinputs.Thisareaitselfisabranchofartificialintelligence(AI)whoseobjectiveistoemulatehumanintelligence.ThefieldofAIisinitsearlieststagesofinfancyintermsofdevelopment,withprogresshavingbeenmuchslowerthanoriginallyanticipated.Theareaofimageanalysis(alsocalledimageunderstanding)isinbe-tweenimageprocessingandcomputervision.Therearenoclearcutboundariesinthecontinuumfromimageprocessingatoneendtocomputervisionattheother.However,oneusefulparadigmistoconsiderthreetypesofcomputerizedprocessesinthiscontinuum:low-,mid-,andhighlevelprocesses.Low-levelprocessesinvolveprimitiveopera-tionssuchasimagepreprocessingtoreducenoise,contrastenhancement,andimagesharpening.Alow-levelprocessischaracterizedbythefactthatbothitsinputsandoutputsareimages.Mid-levelprocessingonimagesinvolvestaskssuchassegmentation(partitioninganimageintoregionsorobjects),descriptionofthoseobjectstoreducethemtoaformsuitableforcomputerprocessing,andclassification(recognition)ofindividualobjects.Amidlevelprocessischaracterizedbythefactthatitsinputsgenerallyareimages,butitsoutputsareattributesextractedfromthoseimages(e.g.,edges,contours,andtheidentityofindividualobjects).Finally,higherlevelprocessinginvolves“makingsense”ofanensembleofrecognizedobjects,asinimageanalysis,and,atthefarendofthecontinuum,performingthecognitivefunctionsnormallyassociatedwithvision.Basedontheprecedingcomments,weseethatalogicalplaceofoverlapbetweenimageprocessingandimageanalysisistheareaofrecognitionofindividualregionsorobjectsinanimage.Thus,whatwecallinthisbookdigitalimageprocessingencompassesprocesseswhoseinputsandoutputsareimagesand,inaddition,encompassesprocessesthatextractattributesfromimages,uptoandincludingtherecognitionofindividualobjects.Asasimpleillustrationtoclarifytheseconcepts,considertheareaofautomatedanalysisoftext.Theprocessesofacquiringanimageoftheareacontainingthetext,preprocessingthatimage,extracting(segmenting)theindividualcharacters,describingthecharactersinaformsuitableforcomputerprocessing,andrecognizingthoseindividualcharactersareinthescopeofwhatwecalldigitalimageprocessinginthisbook.Makingsenseofthecontentofthepagemaybeviewedasbeinginthedomainofimageanalysisandevencomputervision,dependingonthelevelofcomplexityimpliedbythestatement“makingsense.”Aswillbecomeevidentshortly,digitalimageprocessing,aswehavedefinedit,isusedsuccessfullyinabroadrangeofareasofexceptionalsocialandeconomicvalue.Theareasofapplicationofdigitalimageprocessingaresovariedthatsomeformoforganizationisdesirableinattemptingtocapturethebreadthofthisfield.Oneofthesimplestwaystodevelopabasicunderstandingoftheextentofimageprocessingapplicationsistocategorizeimagesaccordingtotheirsource(e.g.,visual,X-ray,andsoon).Theprincipalenergysourceforimagesinusetodayistheelectromagneticenergyspectrum.Otherimportantsourcesofenergyincludeacoustic,ultrasonic,andelectronic(intheformofelectronbeamsusedinelectronmicroscopy).Syntheticimages,usedformodelingandvisualization,aregeneratedbycomputer.Inthissectionwediscussbrieflyhowimagesaregeneratedinthesevariouscategoriesandtheareasinwhichtheyareapplied.ImagesbasedonradiationfromtheEMspectrumarethemostfamiliar,es-peciallyimagesintheX-rayandvisualbandsofthespectrum.Electromagnet-icwavescanbeconceptualizedaspropagatingsinusoidalwavesofvaryingwavelengths,ortheycanbethoughtofasastreamofmasslessparticles,eachtravelinginawavelikepatternandmovingatthespeedoflight.Eachmasslessparticlecontainsacertainamount(orbundle)ofenergy.Eachbundleofenergyiscalledaphoton.Ifspectralbandsaregroupedaccordingtoenergyperphoton,weobtainthespectrumshowninfig.below,rangingfromgammarays(highestenergy)atoneendtoradiowaves(lowestenergy)attheother.ThebandsareshownshadedtoconveythefactthatbandsoftheEMspectrumarenotdistinctbutrathertransitionsmoothlyfromonetotheother.Imageacquisitionisthefirstprocess.Notethatacquisitioncouldbeassimpleasbeinggivenanimagethatisalreadyindigitalform.Generally,theimageacquisitionstageinvolvespreprocessing,suchasscaling.Imageenhancementisamongthesimplestandmostappealingareasofdigitalimageprocessing.Basically,theideabehindenhancementtechniquesistobringoutdetailthatisobscured,orsimplytohighlightcertainfeaturesofinterestinanimage.Afamiliarexampleofenhancementiswhenweincreasethecontrastofanimagebecause“itlooksbetter.”Itisimportanttokeepinmindthatenhancementisaverysubjectiveareaofimageprocessing.Imagerestorationisanareathatalsodealswithimprovingtheappearanceofanimage.However,unlikeenhancement,whichissubjective,imagerestorationisobjective,inthesensethatrestorationtechniquestendtobebasedonmathematicalorprobabilisticmodelsofimagedegradation.Enhancement,ontheotherhand,isbasedonhumansubjectivepreferencesregardingwhatconstitutesa“good”enhancementresult.ColorimageprocessingisanareathathasbeengaininginimportancebecauseofthesignificantincreaseintheuseofdigitalimagesovertheInternet.Itcoversanumberoffundamentalconceptsincolormodelsandbasiccolorprocessinginadigitaldomain.Colorisusedalsoinlaterchaptersasthebasisforextractingfeaturesofinterestinanimage.Waveletsarethefoundationforrepresentingimagesinvariousdegreesofresolution.Inparticular,thismaterialisusedinthisbookforimagedatacompressionandforpyramidalrepresentation,inwhichimagesaresubdividedsuccessivelyintosmallerregions.Compression,asthenameimplies,dealswithtechniquesforreducingthestoragerequiredtosaveanimage,orthebandwidthrequiredtotransmiit.Althoughstoragetechnologyhasimprovedsignificantlyoverthepastdecade,thesamecannotbesaidfortransmissioncapacity.ThisistrueparticularlyinusesoftheInternet,whicharecharacterizedbysignificantpictorialcontent.Imagecompressionisfamiliar(perhapsinadvertently)tomostusersofcomputersintheformofimagefileextensions,suchasthejpgfileextensionusedintheJPEG(JointPhotographicExpertsGroup)imagecompressionstandard.Morphologicalprocessingdealswithtoolsforextractingimagecomponentsthatareusefulintherepresentationanddescriptionofshape.Thematerialinthischapterbeginsatransitionfromprocessesthatoutputimagestoprocessesthatoutputimageattributes.Segmentationprocedurespartitionanimageintoitsconstituentpartsorobjects.Ingeneral,autonomoussegmentationisoneofthemostdifficulttasksindigitalimageprocessing.Aruggedsegmentationprocedurebringstheprocessalongwaytowardsuccessfulsolutionofimagingproblemsthatrequireobjectstobeidentifiedindividually.Ontheotherhand,weakorerraticsegmentationalgorithmsalmostalwaysguaranteeeventualfailure.Ingeneral,themoreaccuratethesegmentation,themorelikelyrecognitionistosucceed.Representationanddescriptionalmostalwaysfollowtheoutputofasegmentationstage,whichusuallyisrawpixeldata,constitutingeitherthebound-aryofaregion(i.e.,thesetofpixelsseparatingoneimageregionfromanother)orallthepointsintheregionitself.Ineithercase,convertingthedatatoaformsuitableforcomputerprocessingisnecessary.Thefirstdecisionthatmustbemadeiswhetherthedatashouldberepresentedasaboundaryorasacompleteregion.Boundaryrepresentationisappropriatewhenthefocusisonexternalshapecharacteristics,suchascornersandinflections.Regionalrepresentationisappropriatewhenthefocusisoninternalproperties,suchastextureorskeletalshape.Insomeapplications,theserepresentationscomplementeachother.Choosingarepresentationisonlypartofthesolutionfortrans-formingrawdataintoaformsuitableforsubsequentcomputerprocessing.Amethodmustalsobespecifiedfordescribingthedatasothatfeaturesofinterestarehighlighted.Description,alsocalledfeatureselection,dealswithextractingattributesthatresultinsomequantitativeinformationofinterestorarebasicfordifferentiatingoneclassofobjectsfromanother.Recognitionistheprocessthatassignsalabel(e.g.,“vehicle”)toanobjectbasedonitsdescriptors.Asdetailedbefore,weconcludeourcoverageofdigitalimageprocessingwiththedevelopmentofmethodsforrecognitionofindividualobjects.SofarwehavesaidnothingabouttheneedforpriorknowledgeorabouttheinteractionbetweentheknowledgebaseandtheprocessingmodulesinFig2above.Knowledgeaboutaproblemdomainiscodedintoanimageprocessingsystemintheformofaknowledgedatabase.Thisknowledgemaybeassim-pleasdetailingregionsofanimagewheretheinformationofinterestisknowntobelocated,thuslimitingthesearchthathastobeconductedinseekingthatinformation.Theknowledgebasealsocanbequitecomplex,suchasaninterrelatedlistofallmajorpossibledefectsinamaterialsinspectionproblemoranimagedatabasecontaininghigh-resolutionsatelliteimagesofaregionincon-nectionwithchange-detectionapplications.Inadditiontoguidingtheoperationofeachprocessingmodule,theknowledgebasealsocontrolstheinteractionbetweenmodules.ThisdistinctionismadeinFig2abovebytheuseofdouble-headedarrowsbetweentheprocessingmodulesandtheknowledgebase,asop-posedtosingle-headedarrowslinkingtheprocessingmodules.EdgedetectionEdgedetectionisaterminologyinimageprocessingandcomputervision,particularlyintheareasoffeaturedetectionandfeatureextraction,torefertoalgorithmswhichaimatidentifyingpointsinadigitalimageatwhichtheimagebrightnesschangessharplyormoreformallyhasdiscontinuities.Althoughpointandlinedetectioncertainlyareimportantinanydiscussiononsegmentation,edgedectectionisbyfarthemostcommonapproachfordetectingmeaningfuldiscountiesingraylevel.Althoughcertainliteraturehasconsideredthedetectionofidealstepedges,theedgesobtainedfromnaturalimagesareusuallynotatallidealstepedges.Insteadtheyarenormallyaffectedbyoneorseveralofthefollowingeffects:1.focalblurcausedbyafinitedepth-of-fieldandfinitepointspreadfunction;2.penumbralblurcausedbyshadowscreatedbylightsourcesofnon-zeroradius;3.shadingatasmoothobjectedge;4.localspecularitiesorinterreflectionsinthevicinityofobjectedges.Atypicaledgemightforinstancebetheborderbetweenablockofredcolorandablockofyellow.Incontrastaline(ascanbeextractedbyaridgedetector)canbeasmallnumberofpixelsofadifferentcoloronanotherwiseunchangingbackground.Foraline,theremaythereforeusuallybeoneedgeoneachsideoftheline.Toillustratewhyedgedetectionisnotatrivialtask,letusconsidertheproblemofdetectingedgesinthefollowingone-dimensionalsignal.Here,wemayintuitivelysaythatthereshouldbeanedgebetweenthe4thand5thpixels.

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Iftheintensitydifferenceweresmallerbetweenthe4thandthe5thpixelsandiftheintensitydifferencesbetweentheadjacentneighbouringpixelswerehigher,itwouldnotbeaseasytosaythatthereshouldbeanedgeinthecorrespondingregion.Moreover,onecouldarguethatthiscaseisoneinwhichthereareseveraledges.Hence,tofirmlystateaspecificthresholdonhowlargetheintensitychangebetweentwoneighbouringpixelsmustbeforustosaythatthereshouldbeanedgebetweenthesepixelsisnotalwaysasimpleproblem.Indeed,thisisoneofthereasonswhyedgedetectionmaybeanon-trivialproblemunlesstheobjectsinthesceneareparticularlysimpleandtheilluminationconditionscanbewellcontrolled.Therearemanymethodsforedgedetection,butmostofthemcanbegroupedintotwocategories,search-basedandzero-crossingbased.Thesearch-basedmethodsdetectedgesbyfirstcomputingameasureofedgestrength,usuallyafirst-orderderivativeexpressionsuchasthegradientmagnitude,andthensearchingforlocaldirectionalmaximaofthegradientmagnitudeusingacomputedestimateofthelocalorientationoftheedge,usuallythegradientdirection.Thezero-crossingbasedmethodssearchforzerocrossingsinasecond-orderderivativeexpressioncomputedfromtheimageinordertofindedges,usuallythezero-crossingsoftheLaplacianorthezero-crossingsofanon-lineardifferentialexpression,aswillbedescribedinthesectionon\o"Edgedetection"differentialedgedetectionfollowingbelow.Asapre-processingsteptoedgedetection,asmoothingstage,typicallyGaussiansmoothing,isalmostalwaysapplied(seealsonoisereduction).Theedgedetectionmethodsthathavebeenpublishedmainlydifferinthetypesofsmoothingfiltersthatareappliedandthewaythemeasuresofedgestrengtharecomputed.Asmanyedgedetectionmethodsrelyonthecomputationofimagegradients,theyalsodifferinthetypesoffiltersusedforcomputinggradientestimatesinthex-andy-directions.Oncewehavecomputedameasureofedgestrength(typicallythegradientmagnitude),thenextstageistoapplyathreshold,todecidewhetheredgesarepresentornotatanimagepoint.Thelowerthethreshold,themoreedgeswillbedetected,andtheresultwillbeincreasinglysusceptibletonoise,andalsotopickingoutirrelevantfeaturesfromtheimage.Converselyahighthresholdmaymisssubtleedges,orresultinfragmentededges.Iftheedgethresholdingisappliedtojustthegradientmagnitudeimage,theresultingedgeswillingeneralbethickandsometypeofedgethinningpost-processingisnecessary.Foredgesdetectedwithnon-maximumsuppressionhowever,theedgecurvesarethinbydefinitionandtheedgepixelscanbelinkedintoedgepolygonbyanedgelinking(edgetracking)procedure.Onadiscretegrid,thenon-maximumsuppressionstagecanbeimplementedbyestimatingthegradientdirectionusingfirst-orderderivatives,thenroundingoffthegradientdirectiontomultiplesof45degrees,andfinallycomparingthevaluesofthegradientmagnitudeintheestimatedgradientdirection.Acommonlyusedapproachtohandletheproblemofappropriatethresholdsforthresholdingisbyusingthresholdingwithhysteresis.Thismethodusesmultiplethresholdstofindedges.Webeginbyusingtheupperthresholdtofindthestartofanedge.Oncewehaveastartpoint,wethentracethepathoftheedgethroughtheimagepixelbypixel,markinganedgewheneverweareabovethelowerthreshold.Westopmarkingouredgeonlywhenthevaluefallsbelowourlowerthreshold.Thisapproachmakestheassumptionthatedgesarelikelytobeincontinuouscurves,andallowsustofollowafaintsectionofanedgewehavepreviouslyseen,withoutmeaningthateverynoisypixelintheimageismarkeddownasanedge.Still,however,wehavetheproblemofchoosingappropriatethresholdingparameters,andsuitablethresholdingvaluesmayvaryovertheimage.Someedge-detectionoperatorsareinsteadbaseduponsecond-orderderivativesoftheintensity.Thisessentiallycapturestherateofchangeintheintensitygradient.Thus,intheidealcontinuouscase,detectionofzero-crossingsinthesecondderivativecaptureslocalmaximainthegradient.Wecancometoaconclusionthat,tobeclassifiedasameaningfuledgepoint,thetransitioningraylevelassociatedwiththatpointhastobesignificantlystrongerthanthebackgroundatthatpoint.Sincewearedealingwithlocalcomputations,themethodofchoicetodeterminewhetheravalueis“significant”ornotidtouseathreshold.Thuswedefineapointinanimageasbeingasbeinganedgepointifitstwo-dimensionalfirst-orderderivativeisgreaterthanaspecifiedcriterionofconnectednessisbydefinitionanedge.Thetermedgesegmentgenerallyisusediftheedgeisshortinrelationtothedimensionsoftheimage.Akeyprobleminsegmentationistoassembleedgesegmentsintolongeredges.Analternatedefinitionifweelecttousethesecond-derivativeissimplytodefinetheedgeponitsinanimageasthezerocrossingsofitssecondderivative.Thedefinitionofanedgeinthiscaseisthesameasabove.Itisimportanttonotethatthesedefinitionsdonotguaranteesuccessinfindingedgeinanimage.Theysimplygiveusaformalismtolookforthem.First-orderderivativesinanimagearecomputedusingthegradient.Second-orderderivativesareobtainedusingtheLaplacian.數(shù)字圖像處理與邊緣檢測(cè)數(shù)字圖像處理數(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)行改良：其二是為使機(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。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)〔x,y〕上的幅值f稱為該點(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)，稱該圖像為數(shù)字圖像。數(shù)字圖像處理是指借用數(shù)字計(jì)算機(jī)處理數(shù)字圖像，值得提及的是數(shù)字圖像是由有限的元素組成的，每一個(gè)元素都有一個(gè)特定的位置和幅值，這些元素稱為圖像元素、畫面元素或像素。像素是廣泛用于表示數(shù)字圖像元素的詞匯。視覺是人類最高級(jí)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(jī)器那么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(duì)非人類習(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〕的界定在初創(chuàng)人之間并沒有一致的看法。有時(shí)用處理的輸入和輸出內(nèi)容都是圖像這一特點(diǎn)來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〕研究的最高目標(biāo)是用計(jì)算機(jī)去模擬人類視覺，包括理解和推理并根據(jù)視覺輸入采取行動(dòng)等。這一領(lǐng)域本身是人工智能的分支，其目的是模仿人類智能。人工智能領(lǐng)域處在其開展過程中的初期階段，它的開展比預(yù)期的要慢的多，圖像分析〔也稱為圖像理解〕領(lǐng)域那么處在圖像處理和計(jì)算機(jī)視覺兩個(gè)學(xué)科之間。從圖像處理到計(jì)算機(jī)視覺這個(gè)連續(xù)的統(tǒng)一體內(nèi)并沒有明確的界線。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，比照度增強(qiáng)和圖像鋒利化。低級(jí)處理是以輸入、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)處理及對(duì)不同目標(biāo)的分類〔識(shí)別〕。中級(jí)圖像處理是以輸入為圖像，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)域符合邏輯的重疊區(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，在研究中，我們界定數(shù)字圖像處理包括輸入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)分析方面的例子來具體說明這一概念。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文界定的數(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(jì)算機(jī)視覺領(lǐng)域考慮問題。這樣，我們定義的數(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。數(shù)字圖像處理的應(yīng)用領(lǐng)域多種多樣，所以文本在內(nèi)容組織上盡量到達(dá)該技術(shù)應(yīng)用領(lǐng)域的廣度。闡述數(shù)字圖像處理應(yīng)用范圍最簡(jiǎn)單的一種方法是根據(jù)信息源來分類〔如可見光、X射線，等等〕。在今天的應(yīng)用中，最主要的圖像源是電磁能譜，其他主要的能源包括聲波、超聲波和電子〔以用于電子顯微鏡方法的電子束形式〕。建模和可視化應(yīng)用中的合成圖像由計(jì)算機(jī)產(chǎn)生。建立在電磁波譜輻射根底上的圖像是最熟悉的，特別是X射線和可見光譜圖像。電磁波可定義為以各種波長(zhǎng)傳播的正弦波，或者認(rèn)為是一種粒子流，每個(gè)粒子包含一定〔一束〕能量，每束能量成為一個(gè)光子。如果光譜波段根據(jù)光譜能量進(jìn)行分組，我們會(huì)得到下列圖1所示的伽馬射線〔最高能量〕到無線電波〔最低能量〕的光譜。如下圖的加底紋的條帶表達(dá)了這樣一個(gè)事實(shí)，即電磁波譜的各波段間并沒有明確的界線，而是由一個(gè)波段平滑地過渡到另一個(gè)波段。圖像獲取是第一步處理。注意到獲取與給出一幅數(shù)字形式的圖像一樣簡(jiǎn)單。通常，圖像獲取包括如設(shè)置比例尺等預(yù)處理。圖像增強(qiáng)是數(shù)字圖像處理最簡(jiǎn)單和最有吸引力的領(lǐng)域。根本上，增強(qiáng)技術(shù)后面的思路是顯現(xiàn)那些被模糊了的細(xì)節(jié)，或簡(jiǎn)單地突出一幅圖像中感興趣的特征。一個(gè)圖像增強(qiáng)的例子是增強(qiáng)圖像的比照度，使其看起來好一些。應(yīng)記住，增強(qiáng)是圖像處理中非常主觀的領(lǐng)域，這一點(diǎn)很重要。圖像復(fù)原也是改良圖像外貌的一個(gè)處理領(lǐng)域。然而，不像增強(qiáng)，圖像增強(qiáng)是主觀的，而圖像復(fù)原是客觀的。在某種意義上說，復(fù)原技術(shù)傾向于以圖像退化的數(shù)學(xué)或概率模型為根底。另一方面，增強(qiáng)以怎樣構(gòu)成好的增強(qiáng)效果這種人的主觀偏愛為根底。彩色圖像處理已經(jīng)成為一個(gè)重要領(lǐng)域，因?yàn)榛诨ヂ?lián)網(wǎng)的圖像處理應(yīng)用在不斷增長(zhǎng)。就使得在彩色模型、數(shù)字域的彩色處理方面涵蓋了大量根本概念。在后續(xù)開展，彩色還是圖像中感興趣特征被提取的根底。小波是在各種分辨率下描述圖像的根底。特別是在應(yīng)用中，這些理論被用于圖像數(shù)據(jù)壓縮及金字塔描述方法。在這里，圖像被成功地細(xì)分為較小的區(qū)域。壓縮，正如其名稱所指的意思，所涉及的技術(shù)是減少圖像的存儲(chǔ)量，或者在傳輸圖像時(shí)降低頻帶。雖然存儲(chǔ)技術(shù)在過去的十年內(nèi)有了很大改良，但對(duì)傳輸能力我們還不能這樣說，尤其在互聯(lián)網(wǎng)上更是如此，互聯(lián)網(wǎng)是以大量的圖片內(nèi)容為特征的。圖像壓縮技術(shù)對(duì)應(yīng)的圖像文件擴(kuò)展名對(duì)大多數(shù)計(jì)算機(jī)用戶是很熟悉的〔也許沒注意〕，如JPG文件擴(kuò)展名用于JPEG〔聯(lián)合圖片專家組〕圖像壓縮標(biāo)準(zhǔn)。形態(tài)學(xué)處理設(shè)計(jì)提取圖像元素的工具，它在表現(xiàn)和描述形狀方面非常有用。這一章的材料將從輸出圖像處理到輸出圖像特征處理的轉(zhuǎn)換開始。分割過程將一幅圖像劃分為組成局部或目標(biāo)物。通常，自主分割是數(shù)字圖像處理中最為困難的任務(wù)之一。復(fù)雜的分割過程導(dǎo)致成功解決要求物體被分別識(shí)別出來的成像問題需要大量處理工作。另一方面，不健壯且不穩(wěn)定的分割算法幾乎總是會(huì)導(dǎo)致最終失敗。通常，分割越準(zhǔn)確，識(shí)別越成功。表示和描述幾乎總是跟隨在分割步驟的輸后邊，通常這一輸出是未加工的數(shù)據(jù)，其構(gòu)成不是區(qū)域的邊緣〔區(qū)分一個(gè)圖像區(qū)域和另一個(gè)區(qū)域的像素集〕就是其區(qū)域本身的所有點(diǎn)。無論哪種情況，把數(shù)據(jù)轉(zhuǎn)換成適合計(jì)算機(jī)處理的形式都是必要的。首先，必須確定數(shù)據(jù)是應(yīng)該被表現(xiàn)為邊界還是整個(gè)區(qū)域。當(dāng)注意的焦點(diǎn)是外部形狀特性〔如拐角和曲線〕時(shí)，那么邊界表示是適宜