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1、ColorizationbyMatrixCompletionShusenWangandZhihuaZhangCollegeofComputerScienceandTechnology,ZhejiangUniversity,China{wssatzju,zhzhang}@gmail.comAbstractGivenamonochromeimageandsomemanuallylabeledpix-els,thecolorizationproblemisacomputer-assistedprocessofaddingc
2、olortothemonochromeimage.Thispaperproposesanovelapproachtothecolorizationproblembyformulat-ingitasamatrixcompletionproblem.Inparticular,takingamonochromeimageandpartsofthecolorpixels(labels)asinputs,wedeveloparobustcolorizationmodelandresorttoanaugmentedLagrang
3、emultiplieralgorithmforsolvingthemodel.Ourapproachisbasedonthefactthatamatrixcanberepresentedasalow-rankmatrixplusasparsematrix.Our(a)Monochrome(b)Labels(c)Recoveredapproachiseffectivebecauseitisabletohandlethepotentialnoisesinthemonochromeimageandoutliersinthe
4、labels.ToFigure1:Colorizationusingourmethod(Low-rank+Local-improvetheperformanceofourmethod,wefurtherincorpo-rateaso-calledlocal-color-consistencyideaintoourmethod.color-consistency)with1%pixelslabeledwithcolors.Empiricalresultsonrealdatasetsareencouraging.twom
5、ajorlimitations.First,colorsaresometimesnotlocalIntroductionconsistent,suchasinsomecomplextextures.Second,thisFortechnicalreasons,oldphotosandfilmsarealllocal-color-consistencyassumptionrequireseachsimilar-monochrome,anditisofgreatinteresttocolorizethosecolorpat
6、chhasatleastonelabeledpixel.Unfortunately,monochromeimagesandfilms.Computerassistedcoloriza-sincesimilar-colorpatchesaresometimesverysmall,theretionhasbecomeanimportantapplicationofartificialintelli-arenumeroussuchpatches,whichmakesithardtoguaran-genceandhasbeenw
7、idelyappliedtofreetechniciansfromteeeachpatchtoincludeonelabeledpixel.manualcolorization.ManymethodshavebeenproposedforInthispaperweproposeanewsemi-supervisedlearningthecolorizationproblemintheliterature(Horiuchi2002;methodfortacklingthecolorizationproblem.Ourw
8、orkisLevin,Lischinski,andWeiss2004;YatzivandSapiro2006;motivatedbytherecentadvancesofmatrixrecoveryandLuanetal.2007).itsextensions.MatrixrecoveryisaclassofproblemsofOnesemin
