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一种梯度正则化稀疏表示的图像超分辨率重建方法.docx

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一种梯度正则化稀疏表示的图像超分辨率重建方法 Title:AGradientRegularizationSparseRepresentation-BasedImageSuper-ResolutionReconstructionMethod Abstract: Imagesuper-resolution(SR)istheprocessofenhancinglow-resolution(LR)imagestohigh-resolution(HR)images.Sparserepresentation-basedSRmethodshaveshownpromisingresultsinachievinghigh-qualityHRimages.Inthispaper,weproposeanovelSRmethodbyincorporatinggradientregularizationintosparserepresentation,aimingtoimprovethereconstructionperformanceofHRimages.Theproposedmethodeffectivelyexploitstheinherentstructureofimagesandenhancesthefinedetailswhileavoidingover-smoothingartifacts. 1.Introduction: Imagesuper-resolutionaimstoreconstructHRimagesfromdegradedLRimages.VariousSRmethodshavebeenproposedovertheyears,includinginterpolation-based,example-based,andlearning-basedapproaches.Sparserepresentationisoneofthelearning-basedmethodsthathasshowngreatpotentialintheSRtask.Byrepresentinganimagepatchasalinearcombinationofafewatomsfromadictionary,sparserepresentationcaneffectivelycapturetheintrinsicsparsityandstructureofnaturalimages.However,existingsparserepresentation-basedSRmethodsstillsufferfromlimitationsinreconstructingfinedetailsandpreservingthesmoothnessoftheHRimages. 2.RelatedWork: Thissectiondiscussestherelevantliteratureonsparserepresentation-basedSRmethods.Itcoversthefundamentalsofsparsecoding,dictionarylearning,andtheirapplicationsinimagesuper-resolution.Additionally,itexplorestheincorporationofgradientregularizationtechniquesinimageSRreconstructiontoaddressthelimitationsoftraditionalsparserepresentation-basedmethods. 3.ProposedMethod: Theproposedmethodintroducesanovelregularizationtermbasedonthegradientsoftheimagepatchtoenhancetheperformanceofsparserepresentation-basedSR.Byleveragingthegradientinformation,theproposedmethodencouragesthereconstructiontopreservetheedgesandtexturesoftheHRimages.Thedetailedformulationandoptimizationprocedureoftheproposedmethodaredescribedinthissection. 4.ExperimentalResults: Tovalidatetheeffectivenessoftheproposedmethod,ext