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图像的稀疏表示及其在图像复原中的应用.docx

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图像的稀疏表示及其在图像复原中的应用 Abstract Imagesparsityisacrucialrepresentationtechniquethathasbecomeincreasinglypopularinimagerestorationapplications.Theuseofsparsityinimagerepresentationanditsapplicationsinrestoringdegradedimageshasquicklybecomeoneofthemostactiveareasofresearchinimageprocessing.Thispaperaimstoprovideanoverviewofimagesparsityanditsuseinimagerestoration,exploringtechniquessuchascompressedsensing,totalvariationregularization,andwavelet-basedsparsitymodels.Thepaperincludesananalysisofcurrentadvancementsinimagerestorationbasedonsparsityandadiscussionofthepotentialfuturedevelopmentsinthefield. Introduction Inthemodernworldoftechnology,digitalimagesareallaroundus.Withtheabilitytocapturehigh-qualityimageswithsmartphonesanddigitalcameras,imageshavebecomeanessentialelementofourdailylives.However,imagesareoftendegradedduetovariousfactorssuchaslowlight,noise,ormotionblur.Restoringdegradedimagesisanessentialgoalinimageprocessing,andtherearevarioustechniquesemployedforimagerestoration.Oneofthemostpromisingtechniquesinrecentyearshasbeentheuseofsparsity-basedrepresentations. Inthispaper,weexploretheconceptofimagesparsityanditsapplicationsinimagerestoration.Weexaminehowtheconceptofimagesparsityhasbeenappliedinvariousimagerestorationtechniquesandhowthesetechniqueshavecontributedtothedevelopmentofthefield.Wealsodiscussthepotentialfuturedevelopmentsthatcanbemadeinthefieldofimagerestorationbasedonsparsity. ImageSparsity Sparsityisafundamentalconceptinsignalprocessingthatreferstoanattributeofsignalswhereonlyafewelementscontributesignificantlytothesignal.Asparsesignalcanberepresentedmoreefficientlythanasignalwithmanynonzeroelements.Sparsityoccursinmanynaturalsignals,includingimages.Animagecanbeconsideredsparsewhenithasafewdominantfeaturesorstructures,whichcanbeexploitedtorepresenttheimageefficiently. CompressedSensing Compressedsensing(CS)isatechniquethatutilizessparsitytoreconstructasignalfromareducednumberofmeasurements.Inimagerestoration,CSisusedtorecoveradegradedimagefromasmallnu