分析成果介紹

ANew DenoisingMethodusingTextureMetrican ptiveStructureVarianceYiweiZhang1,GeLi?2,XiaoqiangGuo3,WenminWang4,RonggangWangSchoolofElectronicandComputerEngineering,PekingUniversityShenzhenGraduateSchoolLishuiRoad2199,NanshanDistrict,Shenzhen,GuangdongProv 1 4 5AcademyofBroadcastingScience,FuxingmenOuterStreet2,XichengDistrict, ,3—Inthispaper,aninnovatedmethodisproposedfordenoising.ThemethodconsistsoftwomajorFirst,anewadaptivesuperpixel texturemetricisproposed. texturemetriciscalculated,whichrelatestodifferentpartsofa stream.Thenanewadaptivestructurevarianceisestimatedbyadoptingfineandcoarsestructures.Finally,anoisefilterbasedontheestimatedweightsofdifferentstructuresina streamisapplied.Bycomparison,theproposedmethodoutperformstraditionalstate-of-artmethods,especiallyinblockartifactsreduction.IndexTerms— denoising,preprocessing,texturemetric,structurevariance,superpixelI.Denoisingtechniqueisawellexploredtopicinthefieldofimageand pre-processing.Itistheactualfoundationforaplentyofapplications,suchasobjectdetection,behavior codecandcomputervision,etc.Overthepastfewdecades,theperformanceofimagedenoisinghasbeenefficientlyimprovedbyemployingamuchmoreelaboratemodelofnaturalimages.Bynow,manygooddenoisingalgo-rithms[1],[2],[3],[4],[5],[6]havebeenproposed,and[7]presentsacomprehensivecomparisonofthem.Eventhough,theoriginalpurposeofdenoisingistoremoveunexpectednoisefromacorruptedimageand .Howevertheartifactscausedbythesealgorithmshavepostedasevereeffectonthequalityofimageand .Thusagooddenoisingmethodshouldintroduceasfewartifactsaspossible.SofarBM3D[4]andBM4D[5]aretwoofthestate-of-artmethodsforimageand denoising.Grousimilar2-Dblockstoa3-DgroupandperformingtransformationarethemajorcontributioninBM3D.Themethodadoptsacollaborativefiltertoperformimagereconstruction,whichretainsbetterdetails.DerivingfromBM3D,BM4Dgroupssimilar3-Dspatiotemporalvolumestoa4-Dgroup.ThosetwomethodsThisprojectwassupportedbyShenzhenPeacockn ),ScienceandTechnologynningProjectofGuangdongProve,(No.2014B )and863projectunderGrant(No.2015AA015905).

performswellinthenoisereduction.However,blockartifactsintroducedbyBM3DandBM4Darestillneedtobeimproved,asshowninFig.1(b).Inthispaper,aninnovatedmethodisproposedwhichcanreducenoisewell,andintheme showslessblockingartifacts.Fig.1.(a)thefirstframeofCrowded3.(b)denoisingresultsofBM4D.(c)denoisingresultsofourmethod.Toachievebetterdenoisingperformance,theapproachiscomposedoftwostages:obtainmentofadaptivesuperpixeltexturemetric;combinationoffineandcoarsestructure.First,weutilizesuperpixelandSingleValue position(SVD)toobtainatexturemetricρforeachpathofeachframe.Bygrouallofpathmetricρ,weobtainabrandnewtexturemetricP.Andalso,Pisutilizedtoestimateproportionoffinestructureandcoarsestructureinthestream.Second,anewadaptivestructurevarianceisobtainedbyadoptingfineandcoarsestructurebasedonP,whichcanbefitwellfordifferentscenarios.Finally,thestreamisobtainedbyapplyinganoisefilterbasedontheestimatedweightsofdifferentstructures.Contribution:twomajorproceduresareproposedinthispaper.First,aninnovateptive texturemetricbasedonsuperpixelisintroduced,whichisrelatedtodifferentpartsofa stream.Second,anewadaptivestructurevarianceisobtainedbyutilizingfineandcoarsestructurestoperformfiltering.Bycomparison,ourproposedmethodoutperformstraditionalstate-of-artmethods,especiallyinblockartifactreduction,asshowninFig.1(c).Theremainderofthispaperisorganizedasfollows.givesthedetailsofourmethod.SectionIgivestheexperimentalresultsofourapproachandcomparisonswithtwostate-of-artalgorithms.Finally,thepaperisconcludedinsectionIV.978-1-5090-5316-2/16/$31.00c2016 VCIP2016,Nov.27–30,2016,.TheProposedTheaimoftheproposedmethodinthispaperistoremovenoisefromcontaminated ,retainmuchmoretexturedetailsandproducefewerblockingartifacts.Inmanyliter-atures,aplentyofnoisemodelsareappliedin orimageprocessing.Inthisstudy,weadoptanadditivewhiteGaussiannoise(AWGN)model.Consideranobserved streamasanoisedimagesequencez:X×Tdefinedasz(x,t)=y(x,t)+η(x,t),x∈X,t∈T wherey(·,·)isamatrixrepresentingtheoriginal(unknown)

AndtheflagindexofeachsuperpixelpathisdefinedF,k=k,k∈Li where,krepresentsthek-thsuperpixelpathof.LabelofSuperpixelGradientPath:HereweGi,k,handGi,k,vasthehorizontalandverticalgradients,krespectively,showninequationGi,k,h,Gi,k,v=Gi,h(x,y),Gi,v(x,y),(x,y)∈XwhereX,kdenotesthesetofcoordinatesof,k.ThenthegradientGi,kof,kisobtainedbyjointingGi,k,hand,η(·,·)～N(0,σ2) .d.AWGN.And(x,t) the3-Dspatiotemporalcoordinateingrayspaceorthe4-D Gi,k= Gi,k,h(m)Gi,k,v(m) ,m∈{1,...,ωi,k} spatiotemporalcoordinateolorspace.Forsimplicity,onlyinthegrayspaceisstudied,thusX?Z2,T?Z. obtainy(·,·),weproposeamethoddesigned whereωi,krepresentsthescalesizeof,k.Gi,kisaωi,k-2matrix.Gi,k,h(m),Gi,k,v(m)denotesthegradientsofy(x,t)=P.?yf(x,t)+(1?P).?yc(x, wherePisatexturemetricmatrixcontainin erallcharac-tersofsuperpixelsfor stream,derivedinsection-

atthecoordinate(xm,AdaptiveSuperpixelSVD:Wedefineacorrelativeco-variancematrixCasyf(·,·)andyc(·,)·areoutputsofnoisefilterswithsucturevarianceσ2andσ2respectively,asshowninsection-B.

CC=

Forsimplicity,weuseGtoreceGi,k.AfterSVDofG, TextureMetricBasedon denoisingfiltering,sometexturedetailsmaybesmooth-filteredcausingunwantedartifacts.To thisdefect,wefirstadoptthemetricPtoestimatetexture.Inspiredby[8],Piscalculatedby

isrepresented

G=U 0

CalculationofOrientationGradient:Whengradientsofneighboursareisotropic,therelativetextureissmooth;when

U ,V= gradientsareanisotropic,thetextureispoignant.Sowe ωi,kgradientoperatorSober(3)and(4)asthebasicoperatorsfortextureestimation.00010000001000000100021

whereUandVarebothorthonormalmatrix,definedinequation(12).s1ands2arethepr ipaleigenvalueofmatrixG.Combining(11)and(12),weobtaDh=

,Dv=

C v2s2+ v11v21(s2? 111

However,Ginpracticeisinterferedbynoise,thusweDh=8?202,Dv=

?torepresenttherealgradientofsuperpixelpathandC??10

representthecorrelativecovariance.Asaresult,equationwhereDhandDvarehorizontalandverticalSoberfilterrespectively.Forthei-thframe ina ,weobtainits

and(13)aremodifiedas(14)and?=?s?10V? gradientGi,h,Gi,v= , ,1≤i≤

2s?2+

C

whereGi,handGi,varehorizontalandverticalgradientsofrespectively,Nfisthenumberof SuperpixelSegmentation:Thereexistsmanyalgorithmstosegmentanimage.Inthispaper,weuse[9]tosplittheframesintomanypaths.FortheframeI,

21 11Herewesetthegradientofsuperpixel-noisepath?=G+ C?=GTG+GTGη+GTG+GT divideitintoNipaths.Thenweobtainsuperpixellabelof,definedas

eCisunknown,soweuseE(?)toestimateE(C)Li={1,..., (?)AssumingnoiseisAWGN,GandGηsatisfytheformulaE(GTGη)=E(GTG)

ηE(GT

2

whereξ=1/2whenusingoperator(3),ξ=3/16whenusingoperator(4).Thus,therelationshipbetweenoriginalandrealisticsinglevalueisobtainedas(

√√

α=v11v21,β

v2+

Fig.2.Expectedvalueof 21,γ=

v?2+v? Inspiredby[10],[11],we

+

Thusweobtaintheexpectedvalueofρi,k describedinequation(27).Fig.2givesthemoreintuitionaldescription

ρ= ?s?12+tocharacterizethenoise-textureofthesuperpixelpath. E(ρi,k)

isan

α,β,γ,ξ,ωi,kareconstantsinafixedpathrespectively,the (ωi,k?1)!!·π

isanonlychangeswiths1,s2andσ2.Now,supposeσ2isconstant,ρwillbelargerwhenthereisaprominenttexture(s1 inthepath.Asaresult,wedefines2?

ThenwesetathresholdτequaltoE(ρi,k),andmark,kasfinestructurepathifρi,k>τandcoarsestructurepathifρi,k<τ.Finally,variancesofall,karecalculated, ρ=s12+ arelabeledas correspondingtofinestructureand correspondingtocoarseasatexturemetricforthesuperpixeObtainmentOfTextureMetric:Considering

2)FilteringwithWeights:First,finestructurevariancefcandcoarsestructurevarianceσ2ofstreamarefcvaryingwithsuperpixelpaths,weuseρi,ktodenotethetexturemetricof,k.Formoreexplicitly,weintroducesuperpixel

respectively

σ2=wf? texturemetricpath?,kcorrespondingto,k,defined

i∈Nf cσ2=w? c

?,k={(x,y)|V(x,y)=ρi,k,(x,y)∈

whereV(x,y)denotesthevalueof atcoordinate(x, wherewfandwcareutilizedtoadjustσ2andσ2,of ThenPisobtainedbyaggregatingall?,k,described thegeneralformulawisdescribed1P={

,k∈

w1+

Combinationof

?=min{mean(σ2?

Inordertoreconstructthestreamwithgood i∈Nf performanceandlessblockartifacts,weadoptanewadaptivestructurevariance.Thisvarianceisestimatedbyadoptingfineandcoarsestructures.Thenanoisefilterbasedontheestimatedweightsofdifferentstructureisapplied.Thedetailsaredescribedby

a,bandcareconstants.Thenthefinestructurestreamyf(·,·)andthecoarsestructurestreamyc(·,·)areobtainedrespectivelyas(yf(x,t)=Filter{z(x,t),ObtainingStructureVariance:First,Weusethecalcu-latedρi,kasateststatistictodecidewhetherasuperpixel

cyc(x,t)=Filter{z(x,t),c

pathhasastrongertexture.Byintroducingeigenvaluesandconditionnumbersofrandommetrics[12],weobtained

whereFilter{z(x,t),σ2}andFilter{z(x,t),σ2} theprocessoffilteringwithσ2andσ2forz(·,· possibilitydensityfp(ρi,k)ofρi,k,described weobserveρi,kcanestimatetheprobabilityofstructures.ThusweutilizePasaweightforfiltering.fp(ρi,k)=(ωi,k?1)ρi,k(1?ρ2 thereconstructedstreamisobtainedbyequationTABLEIComparisonOfPSNRσFig.3.Denoisingresults

streamnamedStandard2.(a)isthefirst

IV.Inthispaper,wepresentaninnovativedenoisingmethodcombininganewadaptivetexturemetricbasedonsuperpixelandanewstructurevariance.Byutilizingthe metrictoweightthefineandcoarse stream,majorartifactsproducedbytraditionalmethodsareeliminateddra-matically.Experimentresultsshowthattheproposedmethodcanachievterperformancecomparedwithtwostate-of-artframeofthe.(b)to(d)respectivelydenotesthefilteringresultsofdifferentalgorithmsasBM3D,BM4D,andours.I.ExperimentSimulationresultsaregivenincomparisonofBM3DandBM4D.Allthemethodsaretestedandcomparedwith40 streamundervariousnoiselevelsand13 stream1.SectionI-AandsectionI-Baresubjectiveandobjectiveassessmentrespectively.ThemajorparametersoftheproposedmethodaresetasTABLE.MajorParametersOfTheProposedδcababSubjectiveAsshowninFig.3(b-c),ourmethodcanadaptivelyadjusttheproportionbetweenfinestructureandcoarsestructuretoretaindetailedtextureandreduceblockartifactsontheprocess denoising.AsshowninFig.3(d),ourmethodretainsasmuchdetailedtextureasBM4DandproducesfewerblockartifactsthanBM4D.ObjectiveTheperformancecomparisonisshowninTABLE1

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