图像处理外文翻译Word文档格式.docx

图像处理外文翻译Word文档格式.docx

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Theprincipalobjectiveofenhancementistoprocessanimagesothattheresultismoresuitablethantheoriginalimageforaspecificapplication.Thewordspecificisimportant,becauseitestablishesattheoutsetthanthetechniquesdiscussedinthischapterareverymuchproblemoriented.Thus,forexample,amethodthatisquiteusefulforenhancingX-rayimagesmaynotnecessarilybethebestapproachforenhancingpicturesofMarstransmittedbyaspaceprobe.Regardlessofthemethodused.However,imageenhancementisoneofthemostinterestingandvisuallyappealingareasofimageprocessing.

Imageenhancementapproachesfallintotwobroadcategories:

spatialdomainmethodsandfrequencydomainmethods.Thetermspatialdomainreferstotheimageplaneitself,andapproachesinthiscategoryarebasedondirectmanipulationofpixelsinanimage.Fouriertransformofanimage.Spatialmethodsarecoveredinthischapter,andfrequencydomainenhancementisdiscussedinChapter4.Enhancementtechniquesbasedonvariouscombinationsofmethodsfromthesetwocategoriesarenotunusual.Wenotealsothatmanyofthefundamentaltechniquesintroducedinthischapterinthecontextofenhancementareusedinsubsequentchaptersforavarietyofotherimageprocessingapplications.

Thereisnogeneraltheoryofimageenhancement.Whenanimageisprocessedforvisualinterpretation,thevieweristheultimatejudgeofhowwellaparticularmethodworks.Visualevaluationofimagequalityisahighlyishighlysubjectiveprocess,thusmakingthedefinitionofa“goodimage”anelusivestandardbywhichtocomparealgorithmperformance.Whentheproblemisoneofprocessingimagesformachineperception,theevaluationtaskissomewhateasier.Forexample,indealingwithacharacterrecognitionapplication,andleavingasideotherissuessuchascomputationalrequirements,thebestimageprocessingmethodwouldbetheoneyieldingthebestmachinerecognitionresults.However,eveninsituationswhenaclear-cutcriterionofperformancecanbeimposedontheproblem,acertainamountoftrialanderrorusuallyisrequiredbeforeaparticularimageenhancementapproachisselected.

3.1Background

Asindicatedpreviously,thetermspatialdomainreferstotheaggregateofpixelscomposinganimage.Spatialdomainmethodsareproceduresthatoperatedirectlyonthesepixels.Spatialdomainprocesseswillbedenotesbytheexpression

（3.1-1）

wheref（x,y）istheinputimage,g（x,y）istheprocessedimage,andTisanoperatoronf,definedoversomeneighborhoodof（x,y）.Inaddition,Tcanoperateonasetofinputimages,suchasperformingthepixel-by-pixelsumofKimagesfornoisereduction,asdiscussedinSection3.4.2.

Theprincipalapproachindefininganeighborhoodaboutapoint（x,y）istouseasquareorrectangularsubimageareacenteredat（x,y）.Thecenterofthesubimageismovedfrompixeltostarting,say,atthetopleftcorner.TheoperatorTisappliedateachlocation（x,y）toyieldtheoutput,g,atthatlocation.Theprocessutilizesonlythepixelsintheareaoftheimagespannedbytheneighborhood.Althoughotherneighborhoodshapes,suchasapproximationstoacircle,sometimesareused,squareandrectangulararraysarebyfarthemostpredominantbecauseoftheireaseofimplementation.

ThesimplestfromofTiswhentheneighborhoodisofsize1×

1（thatis,asinglepixel）.Inthiscase,gdependsonlyonthevalueoffat（x,y）,andTbecomesagray-level（alsocalledanintensityormapping）transformationfunctionoftheform

（3.1-2）

where,forsimplicityinnotation,randsarevariablesdenoting,respectively,thegreyleveloff（x,y）andg（x,y）atanypoint（x,y）.Somefairlysimple,yetpowerful,processingapproachescanbeformulateswithgray-leveltransformations.Becauseenhancementatanypointinanimagedependsonlyonthegreylevelatthatpoint,techniquesinthiscategoryoftenarereferredtoaspointprocessing.

Largerneighborhoodsallowconsiderablymoreflexibility.Thegeneralapproachistouseafunctionofthevaluesoffinapredefinedneighborhoodof（x,y）todeterminethevalueofgat（x,y）.Oneoftheprincipalapproachesinthisformulationisbasedontheuseofso-calledmasks（alsoreferredtoasfilters,kernels,templates,orwindows）.Basically,amaskisasmall（say,3×

3）2-Darray,inwhichthevaluesofthemaskcoefficientsdeterminethenatureofthetypeofapproachoftenarereferredtoasmaskprocessingorfiltering.TheseconceptsarediscussedinSection3.5.

3.2SomeBasicGrayLevelTransformations

Webeginthestudyofimageenhancementtechniquesbydiscussinggray-leveltransformationfunctions.Theseareamongthesimplestofallimageenhancementtechniques.Thevaluesofpixels,beforeandafterprocessing,willbedenotedbyrands,respectively.Asindicatedintheprevioussection,thesevaluesarerelatedbyanexpressionofthefroms=T（r）,whereTisatransformationthatmapsapixelvaluerintoapixelvalues.Sincewearedealingwithdigitalquantities,valuesofthetransformationfunctiontypicallyarestoredinaone-dimensionalarrayandthemappingsfromrtosareimplementedviatablelookups.Foran8-bitenvironment,alookuptablecontainingthevaluesofTwillhave256entries.

Asanintroductiontogray-leveltransformations,whichshowsthreebasictypesoffunctionsusedfrequentlyforimageenhancement:

linear（negativeandidentitytransformations）,logarithmic（logandinverse-logtransformations）,andpower-law（nthpowerandnthroottransformations）.Theidentityfunctionisthetrivialcaseinwhichoutputintensitiesareidenticaltoinputintensities.Itisincludedinthegraphonlyforcompleteness.

3.2.1ImageNegatives

Thenegativeofanimagewithgraylevelsintherange[0,L-1]isobtainedbyusingthenegativetransformationshowshown,whichisgivenbytheexpression

（3.2-1）

Reversingtheintensitylevelsofanimageinthismannerproducestheequivalentofaphotographicnegative.Thistypeofprocessingisparticularlysuitedforenhancingwhiteorgreydetailembeddedindarkregionsofanimage,especiallywhentheblackareasaredominantinsize.

3.2.2LogTransformations

Thegeneralfromofthelogtransformationis

（3.2-2）

Wherecisaconstant,anditisassumedthatr≥0.Theshapeofthelogcurvetransformationmapsanarrowrangeoflowgray-levelvaluesintheinputimageintoawiderrangeofoutputlevels.Theoppositeistrueofhighervaluesofinputlevels.Wewoulduseatransformationofthistypetoexpandthevaluesofdarkpixelsinanimagewhilecompressingthehigher-levelvalues.Theoppositeistrueoftheinverselogtransformation.

Anycurvehavingthegeneralshapeofthelogfunctionswouldaccomplishthisspreading/compressingofgraylevelsinanimage.Infact,thepower-lawtransformationsdiscussedinthenextsectionaremuchmoreversatileforthispurposethanthelogtransformation.However,thelogfunctionhastheimportantcharacteristicthatitcompressesthedynamicrangeofimagecharacteristicsofspectra.Itisnotunusualtoencounterspectrumvaluesthatrangefrom0to106orhigher.Whileprocessingnumberssuchasthesepresentsnoproblemsforacomputer,imagedisplaysystemsgenerallywillnotbeabletoreproducefaithfullysuchawiderangeofintensityvalues.TheneteffectisthatasignificantdegreeofdetailwillbelostinthedisplayofatypicalFourierspectrum.

3.2.3Power-LawTransformations

Power-Lawtransformationshavethebasicfrom

（3.2-3）

Wherecandyarepositiveconstants.SometimesEq.（3.2-3）iswrittenastoaccountforanoffset（thatis,ameasurableoutputwhentheinputiszero）.However,offsetstypicallyareanissueofdisplaycalibrationandasaresulttheyarenormallyignoredinEq.（3.2-3）.PlotsofsversusrforvariousvaluesofyareshowninFig.3.6.Asinthecaseofthelogtransformation,power-lawcurveswithfractionalvaluesofymapanarrowrangeofdarkinputvaluesintoawiderrangeofoutputvalues,withtheoppositebeingtrueforhighervaluesofinputlevels.Unlikethelogfunction,however,wenoticehereafamilyofpossibletransformationcurvesobtainedsimplybyvaryingy.Asexpected,weseeinFig.3.6thatcurvesgeneratedwithvaluesofy＞1haveexactlytheoppositeeffectasthosegeneratedwithvaluesofy＜1.Finally,wenotethatEq.（3.2-3）reducestotheidentitytransformationwhenc=y=1.

Avarietyofdevicesusedforimagecapture,printing,anddisplayrespondaccordingtoasgamma[henceouruseofthissymbolinEq.（3.2-3）].Theprocessusedtocorrectthispower-lawresponsephenomenaiscalledgammacorrection.

Gammacorrectionisimportantifdisplayinganimageaccuratelyonacomputerscreenisofconcern.Imagesthatarenotcorrectedproperlycanlookeitherbleachedout,or,whatismorelikely,toodark.Tryingtoreproducecolorsaccuratelyalsorequiressomeknowledgeofgammacorrectionbecausevaryingthevalueofgammacorrectingchangesnotonlythebrightness,butalsotheratiosofredtogreentoblue.Gammacorrectionhasbecomeincreasinglyimportantinthepastfewyears,asuseofdigitalimagesforcommercialpurposesovertheInternethasincreased.ItisnotInternethasincreased.ItisnotunusualthatimagescreatedforapopularWebsitewillbeviewedbymillionsofpeople,themajorityofwhomwillhavedifferentmonitorsand/ormonitorsettings.Somecomputersystemsevenhavepartialgammacorrectionbuiltin.Also,currentimagestandardsdonotcontainthevalueofgammawithwhichanimagewascreated,thuscomplicatingtheissuefurther.Giventheseconstraints,areasonableappr