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Face image super-resolution through locality-induced support regression
Junjun Jiang; Ruimin Hu; Chao Liang; Zhen Han; Chunjie Zhang
Source PublicationSignal Processing
2014
Issue103Pages:168-183
AbstractIn thispaperweproposeanovelfaceimagesuper-resolution(SR)methodnamed
Locality-inducedSupportRegression(LiSR).Givenalow-resolution(LR)inputpatch,we
learnamappingfunctionbetweenthelocalsupportLRandhigh-resolution(HR)patch
pairs topredictitsHRversion.ThesupportcanbeobtainedfromtheLRorHRpatch
manifolds,whichleadstotwovarietiesofLiSR,namelyLRpatchguidedLiSR(LR-LiSR)and
HR patchguidedLiSR(HR-LiSR).LR-LiSRdirectlylearnsthemappingfunctionbetween
local supportLR/HRpatchpairsgivenaninputLRpatch.AsforHR-LiSR,thesupportanda
mappingfunctionwillbeiterativelylearnedtoupdatethetargetHRpatch.Thekey
advantagesofourproposedframeworkaretwo-fold:(1)thestrongregularizationof
“same representation” of priorwork [1,2] is relaxedtothesamesupport,andhencemuch
flexibilitycanbegiventothelearnedmappingfunction;(2)wedefinethesupportinthe
LR orHRpatchmanifoldspacebyincorporatingthelocalityconstraint,whichcanwell
preserve themanifoldstructureofthetrainingset.Experimentalresultsreportedonboth
simulatedLRfaceimagesandreal-worlddatasetsdemonstratetheeffectivenessofthe
proposed method.
KeywordSuper-resolution Face Image Support Regression Manifold Learning
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15393
Collection类脑智能研究中心
Recommended Citation
GB/T 7714
Junjun Jiang,Ruimin Hu,Chao Liang,et al. Face image super-resolution through locality-induced support regression[J]. Signal Processing,2014(103):168-183.
APA Junjun Jiang,Ruimin Hu,Chao Liang,Zhen Han,&Chunjie Zhang.(2014).Face image super-resolution through locality-induced support regression.Signal Processing(103),168-183.
MLA Junjun Jiang,et al."Face image super-resolution through locality-induced support regression".Signal Processing .103(2014):168-183.
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