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基于生成式对抗网络的图像修复.docx

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基于生成式对抗网络的图像修复 标题：GeneratingImageRestorationusingGenerativeAdversarialNetworks Abstract: Imagerestorationisafundamentaltaskincomputervision,aimedatrecoveringcorruptedordamagedimagestorestoretheiroriginalappearance.Inrecentyears,generativeadversarialnetworks(GANs)haveshowngreatpotentialinimagegenerationtasks.Inthispaper,weexploretheapplicationofGANsforimagerestoration,specificallyfocusingontherestorationofcorruptedorincompleteimages.Weproposeanovelframeworkthatcombinesageneratornetworkwithadiscriminatornetworktoachievestate-of-the-artimagerestorationresults.Experimentalresultsdemonstratethatourproposedmethodcaneffectivelyenhanceimagerestorationqualityandproducevisuallyappealingandrealisticresults. 1.Introduction Imagerestorationaimstorecoverimagesthatmayhavebeencorruptedbyvariousfactorssuchasnoise,artifacts,ormissinginformation.Traditionalrestorationmethodsareoftenlimitedinhandlingcompleximagedistortionsandmayresultinlossofimagedetailsorintroduceartifacts.WiththeemergenceofGANs,thetaskofimagerestorationhasgainedrenewedattention,astheyhaveshownremarkablesuccessingeneratingrealisticimages.Byleveragingtheadversarialtrainingprocess,GANscangeneratehigh-qualityandvisuallypleasingrestoredimagesbylearningfromalargedatasetofpairedcleanandcorruptedimages. 2.RelatedWork Wereviewtherecentadvancementsinimagerestorationtechniques,includingtraditionalmethodsbasedonfiltering,inpainting,andsuper-resolution,aswellasstate-of-the-artdeeplearning-basedapproaches.WediscussthelimitationsofexistingmethodsandhighlighttheadvantagesofusingGANsforimagerestorationtasks. 3.Methodology WepresentourproposedimagerestorationframeworkbasedonGANs.Ourframeworkconsistsofageneratornetworkthataimstotransformcorruptedimagesintohigh-qualityrestoredimages,andadiscriminatornetworkthatdistinguishesbetweenthegeneratedimagesandthegroundtruthimages.Weintroduceanovellossfunctionthatcombinescontentlossandadversariallosstoguidethegeneratornetworkinproducingrealisticandvisuallycoherentresults.Wealsodiscussthearchitect