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基于局部边沿方向的非局部均值图像去噪算法（英文）.docx

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基于局部边沿方向的非局部均值图像去噪算法（英文） Title:Non-localMeanImageDenoisingAlgorithmBasedonLocalEdgeDirections Abstract: Imagedenoisingisafundamentaltaskinthefieldofimageprocessingandhasawiderangeofapplications.Non-localmeans(NLM)algorithmhasbeenwidelyemployedforimagedenoisingduetoitsabilitytoeffectivelypreserveimagedetails.However,thetraditionalNLMalgorithmignorestheimportanceoflocaledgedirections,whichcansignificantlyaffectthedenoisingperformance.Inthispaper,weproposeanovelnon-localmeanimagedenoisingalgorithmbasedonlocaledgedirections.Theproposedalgorithmincorporateslocaledgedirectionsintothesimilaritycalculation,leadingtoimproveddenoisingperformanceinpreservingimagedetailsandsuppressingnoise. 1.Introduction Imagedenoisingisacrucialpreprocessingstepinmanyapplications,suchasimagerestoration,medicalimaging,andcomputervision.Thegoalofimagedenoisingistoremovenoisewhilepreservingimportantimagefeaturesanddetails.Variousalgorithmshavebeendevelopedforimagedenoising,includingspatialfiltering,frequencydomainmethods,andnon-localmeans. 2.Non-localMeansAlgorithm Thenon-localmeans(NLM)algorithmisawidelyusedmethodforimagedenoising.Itexploitstheredundancyandsimilaritywithinanimagetoeffectivelyremovenoisewhilepreservingimagedetails.TheNLMalgorithmcalculatespixelsimilaritybasedonthesimilarityofneighborhoods,resultinginbetterdenoisingperformancecomparedtotraditionalspatialfilteringmethods. 3.IncorporatingLocalEdgeDirections Edgesplayacriticalroleinimagedenoising,astheyrepresentimportantimagefeaturesanddetails.However,thetraditionalNLMalgorithmdoesnotconsiderthedirectionalityofedges,whichcanleadtoinadequatesuppressionofnoisearoundedges.Inourproposedalgorithm,weincorporatelocaledgedirectionsintothesimilaritycalculation.Thisisachievedbyincorporatingedgeinformationfromgradientmaps,whichprovideinformationabouttheorientationofedgesinlocalneighborhoods. 4.ProposedNon-localMeanImageDenoisingAlgorithm Theproposedalgorithmconsistsofthefollowingsteps: a)Computelocaledgedirectionsusinggradientmaps. b)Calculatet