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基于改进型CNN的多聚焦图像融合方法.docx

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基于改进型CNN的多聚焦图像融合方法 Title:Multi-FocusImageFusionMethodbasedonEnhancedConvolutionalNeuralNetworks Abstract: Imagefusionisacrucialtechniqueincomputervisionandimageprocessingthatcombinesmultipleimagestoobtainasinglecompositeimagewithhigherqualityandricherinformation.Thispaperproposesanovelmulti-focusimagefusionmethodbasedonanenhancedConvolutionalNeuralNetwork(CNN).Theproposedmethodemploysamulti-scalestrategytoextractfeaturesatdifferentlevelsandexploitsattentionmechanismstofocusonthemostsalientregions.Experimentalresultsdemonstratethattheproposedmethodoutperformsexistingimagefusionmethodsintermsofvisualqualityandobjectiveevaluationmetrics. 1.Introduction Imagefusionaimstointegratecomplementaryinformationfrommultipleimagestoproduceafusedimagethatprovidesamorecompleteandaccuraterepresentationofthescene.Variousapplicationssuchassurveillancesystems,roboticsystems,andmedicalimagingrequirehigh-qualityimagefusiontechniques.Amongdifferenttypesofimagefusion,multi-focusimagefusionisparticularlychallengingasitinvolvesfusingimageswithdifferentfocusestoobtainasharpandclearcompositeimage. 2.RelatedWork Thissectionbrieflyreviewsexistingmulti-focusimagefusionmethods,includingtraditionalmethodsbasedonhandcraftedfeaturesanddeeplearning-basedmethods.Whiletraditionalmethodsoftenrelyonspecificassumptionsandheuristics,deeplearning-basedmethodshaveshownpromisingperformancebylearningfeaturerepresentationsautomaticallythroughConvolutionalNeuralNetworks(CNNs). 3.ProposedMethod Theproposedmulti-focusimagefusionmethodconsistsoftwomainsteps:featureextractionandfusion.Inthefeatureextractionstep,anenhancedCNNisemployedtoextractfeaturesatdifferentscales,capturingbothlocalandglobalinformationoftheinputimages.TheenhancedCNNisdesignedwithmultipleconvolutionallayersandresidualconnections,enablingittolearnrichanddiscriminativefeatures.Thefusionsteputilizesattentionmechanismstofocusonregionswithhighsaliency,emphasizingimportantfeaturesinthefinalfusedimage. 4.ExperimentalResults Toevaluatetheperformanceofthep