深度學(xué)習(xí)下的圖像視頻處理技術(shù)

技術(shù)創(chuàng)新，變革未來

深度學(xué)習(xí)下的圖像視頻處理技術(shù)看得更清，看得更懂目錄夜景增強圖像視頻去模糊視頻超分辨率1.

夜景圖像增強Takingphotosis

easyAmateurphotographerstypicallycreateunderexposedphotosPhotoEnhancementis

requiredImage

EnhancementInput“AutoEnhance”on

iPhone“AutoTone”in

LightroomOursExistingPhotoEditing

ToolsRetinex-based

MethodsLIME:[TIP

17]WVM:[CVPR

16]JieP:[ICCV17]Learning-based

MethodsHDRNet:[SIGGRAPH

17]White-Box:[ACMTOG

18]Distort-and-Recover:[CVPR

18]DPE:[CVPR

18]Previous

WorkInputWVM

[CVPR’16]JieP

[ICCV’17]HDRNet

[Siggraph’17]DPE

[CVPR’18]White-Box

[TOG’18]Distort-and-Recover

[CVPR’18]OursLimitationsofPrevious

MethodsIlluminationmapsfornaturalimagestypicallyhaverelativelysimpleformswithknown

priors.Themodelenablescustomizingtheenhancementresultsbyformulatingconstraintsonthe

illumination.WhyThis

Model?Advantage:EffectiveLearningandEfficient

LearningNetwork

ArchitectureInputNa?ve

RegressionExpert-retouchedAblation

StudyMotivation:Thebenchmarkdatasetiscollectedforenhancinggeneralphotosinsteadofunderexposedphotos,andcontainsasmallnumberofunderexposedimagesthatcoverlimitedlighting

conditions.Our DatasetQuantitative

Comparison:

Our DatasetMethodPSNRSSIMHDRNet26.330.743DPE23.580.737White-Box21.690.718Distort-and-Recover24.540.712Oursw/o????????,w/o????????,w/o

????????27.020.762Ourswith????????,w/o????????,w/o

????????28.970.783Ourswith????????,with????????,w/o

????????30.030.822Ours30.970.856MethodPSNRSSIMHDRNet28.610.866DPE24.660.850White-Box23.690.701Distort-and-Recover28.410.841Oursw/o????????,w/o????????,w/o

????????28.810.867Ourswith????????,w/o????????,w/o

????????29.410.871Ourswith????????,with????????,w/o

????????30.710.884Ours30.800.893QuantitativeComparison:MIT-Adobe

FiveKVisual

Comparison:

Our DatasetInputJiePHDRNetDPEWhite-boxDistort-and-RecoverOur

resultExpert-retouchedVisualComparison:MIT-Adobe

FiveKInputJiePHDRNetDPEWhite-boxDistort-and-RecoverOur

resultExpert-retouchedMoreComparisonResults:User

StudyInputWVMJiePHDRNetDPEWhite-BoxDistort-and-RecoverOur

resultLimitaionInputOur

result演示者2019-05-08

03:51:53--------------------------------------------Ourworkalsoexistssomelimitations,

thefirstlimitationistheregionisalmostblackwithoutanytraceoftexture.Wecanseethetoptwoimages.Thesecondlimitationisourmethoddoen’tclearnoiseintheenhanced

result.More

ResultsInputWhite-boxDistort-and-RecoverOur

resultExpert-retouchedJiePHDRNetDPEMore

ResultsInputWhite-boxDistort-and-RecoverOur

resultExpert-retouchedJiePHDRNetDPEMore

ResultsInputWhite-boxDistort-and-RecoverOur

resultExpert-retouchedJiePHDRNetDPEMore

ResultsInputWhite-boxDistort-and-RecoverOur

resultExpert-retouchedJiePHDRNetDPEMore

ResultsInputWVMJiePHDRNetDPEWhite-BoxDistort-and-RecoverOur

resultMore

ResultsInputWVMJiePHDRNetDPEWhite-BoxDistort-and-RecoverOur

resultMore

ResultsOur

resultiPhoneLightroomInputMore

ResultsOur

resultiPhoneLightroomInput2.

視頻超分辨率Oldand

FundamentalSeveraldecadesago[Huangetal,1984]→nearrecentMany

ApplicationsHDvideogenerationfromlow-res

sourcesMotivation演示者2019-05-08

03:51:55--------------------------------------------Thetargetofvideosuper-resolutionistoincreasetheresolutionofvideoswithrichdetails.

[click]Itisanoldandfundamentalproblem

thathasbeenstudiedsinceseveraldecadesago.

[click]VideoSRenablesmanyapplications,

suchasHigh-definitionvideogenerationfromlow-ressources.

[click]32Oldand

FundamentalSeveraldecadesago[Huangetal,1984]→nearrecentMany

ApplicationsHDvideogenerationfromlow-res

sourcesVideoenhancementwith

detailsMotivation演示者2019-05-08

03:51:55--------------------------------------------[click]Videoenhancementwith

details.Inthisexample,charactersontheroofandtexturesofthetreeinSRresultaremuchclearertheninput.

[click]33Oldand

FundamentalSeveraldecadesago[Huangetal,1984]→nearrecentMany

ApplicationsHDvideogenerationfromlow-res

sourcesVideoenhancementwith

detailsText/objectrecognitioninsurveillance

videosMotivation演示者2019-05-08

03:51:55--------------------------------------------[click]Andalso,itcanbenefittextorobject

recognitioninlow-qualitysurveillance

videos.Inthisexample,numbersonthecar

becomerecognizableonlyinthesuper-resolved

result.34ImageSRTraditional:[Freemanetal,2002],[Glasneretal,2009],[Yangetal,2010],etc.CNN-based:SRCNN[Dongetal,2014],VDSR[Kimetal,2016],FSRCNN[Dongetal,2016],

etc.Video

SRTraditional:3DSKR[Takedaetal,2009],BayesSR[Liuetal,2011],MFSR[Maetal,2015],

etc.CNN-based:DESR[Liaoetal,2015],VSRNet[Kappeler,etal,2016],

[Caballeroetal,2016],

etc.35Previous

Work演示者2019-05-08

03:51:56--------------------------------------------Previously,lotsofworkandmethods

havebeenproposedinsuper-resolution.

[click]Welistseveralrepresentativemethods

here.EffectivenessHowtomakegooduseofmultiple

frames?Remaining

Challenges39DatafromVid4[CeLiuet

al.]Bicubic

x4MisalignmentLargemotionOcclusion演示者2019-05-08

03:51:56--------------------------------------------Althoughvideosrhaslongbeen

studied,therearestillremainingchallenges

inthistask.

[click]Themostimportantoneis

effectiveness.

[click]Howtomakegooduseofmultipleframes?

[click][click]Asshowninthisexample,objectsinneighboringframesarenotaligned.Andinsomeextremecases,there

evenexistlargemotionorocclusion,whichareveryhardtohandle.Soaremultipleframesusefulorharmfultosuper-resolution?EffectivenessHowtomakegooduseofmultipleframes?Arethegenerateddetails

real?Remaining

Challenges40Image

SRBicubic

x4演示者2019-05-08

03:51:56--------------------------------------------[click]Ontheotherhand,arethegenerated

detailsrealdetails?

[click][click]CNN-basedSRmethodsincorporate

externaldata.Usingonlyoneframe,theycanalsoproducesharpstructures.Inthis

example,ontheright-hand-side,oneSRmethodgeneratessomeclearwindowpatternsonthebuilding,[click]buttheyarefarfromrealonthe

left.Theproblemis,detailsfromexternal

data,maynotbetrueforinputimage.EffectivenessHowtomakegooduseofmultipleframes?Arethegenerateddetails

real?Remaining

ChallengesImage

SRTruth演示者2019-05-08

03:51:56--------------------------------------------[click]Ontheotherhand,arethegenerated

detailsrealdetails?

[click][click]CNN-basedSRmethodsincorporate

externaldata.Usingonlyoneframe,theycanalsoproducesharpstructures.Inthis

example,ontheright-hand-side,oneSRmethodgeneratessomeclearwindowpatternsonthebuilding,[click]buttheyarefarfromrealonthe

left.Theproblemis,detailsfromexternal

data,maynotbetrueforinputimage.38EffectivenessHowtomakegooduseofmultipleframes?Arethegenerateddetails

real?ModelIssuesOnemodelforonesettingRemaining

ChallengesVDSR[Kimetal.,

2016]ESPCN[Shietal.,

2016]VSRNet[Kappeleretal,

2016]演示者2019-05-08

03:51:56--------------------------------------------[click]Therearealsomodelissuesincurrent

methods.

[click]ForallrecentCNN-basedSRmethods,

modelparametersarefixedforcertainscalefactors,ornumberofframes.Ifyouwanttochangescalefactors,youneedtochangenetworkconfigurationandtrainanother

one.39EffectivenessHowtomakegooduseofmultipleframes?Arethegenerateddetails

real?ModelIssuesOnemodelforonesettingIntensiveparametertuningSlow40Remaining

Challenges演示者2019-05-08

03:51:56--------------------------------------------[click][click]AndmosttraditionalvideoSRmethods

involveintensiveparametertuningandmaybeslow.Alltheissuesmentionedabovepreventthemfrompractical

usage.AdvantagesBetteruseofsub-pixel

motionPromisingresultsbothvisuallyand

quantitativelyFullyScalableArbitraryinputsizeArbitraryscale

factorArbitrarytemporal

frames41Our Method演示者2019-05-08

03:51:57--------------------------------------------Thegoalsofourmethodareasfollows.

[click]Wearetryingtomakebetteruse

of

sub-pixelmotionbetweenframesandproducehigh-qualityresultswithrealdetails.

[click]Wealsohopethedesignedframework

befullyscalable,intermsofinputimagesize,scalefactorsandframenumber.

[click]45DatafromVid4[CeLiuet

al.]演示者2019-05-08

03:51:57--------------------------------------------Hereisonevideo

example.Characters,numbersandtextures

arehardtorecognizeinbicubicresult.Andoursresultsaremuchbetterand

clearer.Motion

EstimationOur Method????????????0????????ME????????→0演示者2019-05-08

03:51:57--------------------------------------------Duetotimelimit,herewebrieflydescribe

ourmethod.Audiencesarewelcometoourpostersessionformoredetails.Ourmethodcontains3

components.[click]Thefirstmoduleisamotionestimation

network.

[click]Thismoduletake2low-resimages

asinput.

[click]Andoutputsalow-resmotionfield.

[click]43Sub-pixelMotionCompensation(SPMC)

LayerOur Method????????????0????????ME????????→0SPMC演示者2019-05-08

03:51:57--------------------------------------------[click]Thesecondmoduleisnewlydesigned.

Wecallitsub-pixelmotioncompensationlayer.

[click]Thismoduletakesasinputtheithlow-resframeandtheestimatedmotionfield.Theoutputofthismoduleisahigh-res

image.Unlikepreviousmethods,thislayer

simultaneouslyachieveresolutionenhancementandmotioncompensation,whichcanbetterkeepsubpixelinformationin

frames.44DetailFusion

NetOur Method????????????0????????ME????????→0SPMCEncoderDecoderConvLSTM????=?????

1????=????+

1skip

connections演示者2019-05-08

03:51:57--------------------------------------------[click]Inthelaststage,wedesignaDetail

FusionNetworktocombineallframes.

[click]Hereweuseencoder-decoder

structure

inthismodule,sinceitisprovedveryeffectiveinimageregressiontasks.Skipconnectionsareusedforbetterconvergence.[click]Theimportantchangeisthat,we

insert

aconvLSTMmoduleinsiderthenetwork.Itisanaturalchoicesincewe

are

handlingsequentialinputsandhopingtoutilizetemporalinformation.[click]TheConvLSTMconsidersinformation

fromprevioustimestep,andpasshiddenstatetonexttime

step.45ArbitraryInput

Size????????????0????????ME????????→0SPMCEncoderConvLSTM????=?????

1????=????+

1skip

connectionsInput

size:Fully

convolutionalDecoder演示者2019-05-08

03:51:57--------------------------------------------Ourproposedframeworkhastheadvantageoffullyscalability.

[click]Inputvideosmaybeofdifferentsizes

inpractise.

[click]Sinceournetworkisfullyconvolutional,

itcannaturalhandle

this.46ArbitraryScale

Factors2×3×4×ParameterFree????????????0????????ME????????→0SPMCEncoderConvLSTM????=?????

1????=????+

1skip

connectionsDecoder演示者2019-05-08

03:51:58--------------------------------------------[click]Whendealingwithdifferentscale

factors,previousnetworksneedtochangenetworkparameters.[click]Ournetworkisdifferentsincethe

resolutionincreasehappensinSPMClayer,anditisparameterfree.[click]Thispropertyenablesustouseone

singlemodelconfigurationtohandleallscalefactors,includingnon-integer

values.47ArbitraryTemporal

Length3

frames5

frames????????????0????????ME????????→0SPMCEncoderConvLSTM????=?????

1????=????+

1skip

connectionsDecoder演示者2019-05-08

03:51:58--------------------------------------------[click]Forpracticalsystems,wemaywant

tochoosethenumberofframesintestingphase,inordertoachievebalancebetweenqualityandefficiency.OurframeworkusesConvLSTMtohandleframesinasequential

way.[click]Therefore,itcanacceptarbitrarytemporal

length.48Detailsfrom

multi-framesAnalysisOutput

(identical)3

identicalframes演示者2019-05-08

03:51:58--------------------------------------------Wedoanalysistoevaluateourmethod.

[click]First,areourrecovereddetailsreal?

[click]Hereweusethreeidenticalframes

asinputtoour

network.Theinformationcontainedinthisinput

isnomorethanonesinglelow-resimage.

[click]Asexpected,althoughsharper,the

outputcontainsnomoredetails.Andthecharactersandlogoarestill

unrecognizable.49Detailsfrom

multi-framesAnalysis3

consecutiveframesOutput

(consecutive)Output

(identical)50演示者2019-05-08

03:51:58--------------------------------------------[click]However,ifwetake3consecutive

framesfromthevideoasinput.

[click]Ournetworkproducesmuchbetter

results.Charactersandlogoareverycleartobe

read.Thisexperimentprovesthatthesharp

structuresrecoveredcomefromrealinformationofinputs,ratherthenfromexternalinformationinthenetwork.WewillbesafetotrusttheSR

results.AblationStudy:SPMCLayerv.s.BaselineAnalysisOutput

(baseline)????????→0BWResizeBackward

warping+Resize(baseline)51演示者2019-05-08

03:51:58--------------------------------------------[click]Inthenextexperiment,wedoablation

studyofourSPMClayer.

[click]WesubstituteSPMClayerwitha

baselinemodule,thatisabackwardwarpingfollowedby

upsampling.Thisbaselinemethodcanalsocompensate

motionandincreaseresolution.

ItiswidelyadoptedinpreviousCNN-basedmethods.

[click]Inthisexample,thetilesontheroof

containseverelyfalsestructuresdueto

aliasing.AblationStudy:SPMCLayerv.s.BaselineAnalysisOutput

(SPMC)????????→0SPMCSPMCOutput

(baseline)52演示者2019-05-08

03:51:58--------------------------------------------[click]WithourdesignedSPMClayer,[click]thestructuresoftilesintheresult

areveryfaithfultotheground

truth.Webelieveonlybyproperlyhandling

motioninsub-pixelprecision,

canwerecovergood

results.ComparisonsBicu5b6ic

x4演示者2019-05-08

03:51:59--------------------------------------------Wefurthercomparewithcurrentstate-of-the-arts.

Thisisthebicubicinterpolatedversionof

input.Thewindowsandglassofthebuilding

areseverely

blurred.ComparisonsBayesSR[Liuetal,257011;Maetal.,

2015]演示者2019-05-08

03:51:59--------------------------------------------TheresultofBayesianSRissharp,

butthestructuresarestill

missing.ComparisonsDESR[Liao58etal.,

2015]演示者2019-05-08

03:51:59--------------------------------------------Draft-ensembleSRrecoversafew

details,butwith

artifacts.ComparisonsVSRNet[Kapp5e9leretal,

2016]演示者2019-05-08

03:51:59--------------------------------------------OnerecentCNN-basedVSRNetproduces

smooth

result.ComparisonsOu60rs演示者2019-05-08

03:51:59--------------------------------------------Visually,ourresultismuchbetter.Theedgesofthebuildingsandwindowsareeasytodistinguish.Wethengobackto

input.[click]ComparisonsBicu6b1ic

x4演示者2019-05-08

03:51:59--------------------------------------------Then

our

results.[click]ComparisonsOu62rs演示者2019-05-08

03:51:59--------------------------------------------Thechangesare

obvious.Running

Time60演示者2019-05-08

03:52:00--------------------------------------------Wecomparerunningtimewithmost

ofthecurrentmethods

[click]BayesSR[Liuetal,

2011]Running

Time2hour/

frameFrames:

31ScaleFactor:

4×演示者2019-05-08

03:52:00--------------------------------------------BayesianSRmethodneeds2hours

toproduceoneframe,asreportedintheir

paper.61MFSR[Maetal,

2015]Running

Time1062min/

frameFrames:

31ScaleFactor:

4×演示者2019-05-08

03:52:00--------------------------------------------MFSRmethodrequires10minper

frame.DESR[Liaoetal,

2015]Running

TimeFrames:

31ScaleFactor:

4×638min/

frame演示者2019-05-08

03:52:00--------------------------------------------DraftensambleSRrequires8

minutes.VSRNet[Kappeleretal,

2016]Running

Time4064s/

frameFrames:5ScaleFactor:

4×演示者2019-05-08

03:52:00--------------------------------------------VSRNetneeds40secondper

frame.Ours(5

frames)Running

Time0.19s

/frameFrames:

5ScaleFactor:

4×65演示者2019-05-08

03:52:00--------------------------------------------Ourframeworkismuchfastersince

allcomponentscanbeefficientlycomputedonGPU.Itrequires0.19susingneighboring5frames.Ours(3

frames)Running

Time0.14s

/frameFrames:

3ScaleFactor:

4×66演示者2019-05-08

03:52:00--------------------------------------------Itcanbefurtheracceleratedto0.14

secondifweuse3

frames.More

Results67演示者2019-05-08

03:52:01--------------------------------------------Hereweshowmorevideo

results.演示者2019-05-08

03:52:01--------------------------------------------Inthisfirstresult,ourmethodworks

verywell,especiallyonedgesofthe

building.68演示者2019-05-08

03:52:01--------------------------------------------Inthenextresult,tilesofthetemple

andcarvesonthelamparemostlyrecovered.69SummaryEnd-to-end&fully

scalableNewSPMC

layerHigh-quality&fast

speed????????????0????????ME????????→0SPMCEncoderConvLSTM????=?????

1????=????+

1skip

connectionsDecoder演示者2019-05-08

03:52:01--------------------------------------------Insummary,weproposeanewend-to-end

CNN-basedframeworkforvideoSR,whichisfullyscalable.

[click]OurframeworkincludesanewSPMC

layerthatcanbetterhandleinter-framemotion.

[click]Ourmethodproduceshigh-quality

resultswithfast

speed.703.

圖像視頻去模糊圖像去模糊問題75Datafromprevious

work演示者2019-05-08

03:52:02--------------------------------------------Thetargetofvideosuper-resolutionistoincreasetheresolutionofvideoswithrichdetails.

[click]Itisanoldandfundamentalproblem

thathasbeenstudiedsinceseveraldecadesago.

[click]VideoSRenablesmanyapplications,

suchasHigh-definitionvideogenerationfromlow-ressources.

[click]DifferentBlurAssumptionsUniform:[Fergusetal,2006],[Shanetal,2009],[Choetal,2009],[Xuetal,2010],

etc.Previous

Work76Datafrom[Xuetal,

2010]演示者2019-05-08

03:52:02--------------------------------------------Previously,lotsofworkandmethods

havebeenproposedinsuper-resolution.

[click]Welistseveralrepresentativemethods

here.DifferentBlurAssumptionsNon-uniform:[Whyteetal,2010],[Hirschetal,2011],[Zhengetal,2013],

etc.Previous

Work77Datafrom[Whyteetal,

2010]演示者2019-05-08

03:52:02--------------------------------------------Previously,lotsofworkandmethods

havebeenproposedinsuper-resolution.

[click]Welistseveralrepresentativemethods

here.DifferentBlurAssumptionsDynamic:[Kimetal,2013],[Kimetal,2014],[Nahetal,2017],

etc.Previous

Work78Datafrom[Kimetal,

2013]演示者2019-05-08

03:52:02--------------------------------------------Previously,lotsofworkandmethods

havebeenproposedinsuper-resolution.

[click]Welistseveralrepresentativemethods

here.Learning-based

methodsEarlymethods:[Sunetal,2015],[Schuleretal,2016],[Xiaoetal,2016],etc.Substituteafewtraditionalmoduleswithlearned

parametersMorerecent:[Nahetal,2017],[Kimetal,2017],[Suetal,2017],[Wiescholleketal,2017]Network:encoder-decoder,multi-scale,

etc.Previous

Work79演示者2019-05-08

03:52:02--------------------------------------------Previously,lotsofworkandmethods

havebeenproposedinsuper-resolution.

[click]Welistseveralrepresentativemethods

here.ComplicatedReal-world

BlurRemaining

Challenges80DatafromGOPRO

dataset演示者2019-05-08

03:52:02--------------------------------------------Althoughvideosrhaslongbeen

studied,therearestillremainingchallenges

inthistask.

[click]Themostimportantoneis

effectiveness.

[click]Howtomakegooduseofmultipleframes?

[click][click]Asshowninthisexample,objectsinneighboringframesarenotaligned.Andinsomeextremecases,there

evenexistlargemotionorocclusion,whichareveryhardtohandle.Soaremultipleframesusefulorharmfultosuper-resolution?Ill-posedProblem&UnstableSolversArtifacts:ringing,noise,

etc.Remaining

Challenges81Datafrom[Moslehetal,

2014]inaccurate

kernelsinaccurate

modelsinformationlossunstable

solvers演示者2019-05-08

03:52:02--------------------------------------------[click]Ontheotherhand,arethegenerated

detailsrealdetails?

[click][click]CNN-basedSRmethodsincorporate

externaldata.Usingonlyoneframe,theycanalsoproducesharpstructures.Inthis

example,ontheright-hand-side,oneSRmethodgeneratessomeclearwindowpatternsonthebuilding,[click]buttheyarefarfromrealonthe

left.Theproblemis,detailsfromexternal

data,maynotbetrueforinputimage.EfficientNetwork

StructureU-Netorencoder-decodernetwork[Suetal,

2017]Remaining

Challenges82InputOutputconvskip

connection演示者2019-05-08

03:52:03--------------------------------------------[click]Ontheotherhand,arethegenerated

detailsrealdetails?

[click][click]CNN-basedSRmethodsincorporate

externaldata.Usingonlyoneframe,theycanalsoproducesharpstructures.Inthis

example,ontheright-hand-side,oneSRmethodgeneratessomeclearwindowpatternsonthebuilding,[click]buttheyarefarfromrealonthe

left.Theproblemis,detailsfromexternal

data,maynotbetrueforinputimage.EfficientNetwork

StructureMulti-scaleorcascadedrefinementnetwork[Nahetal,

2017]Remaining

Challenges83Outputconvconvinputfine

stagecoarse

stageresize

up演示者2019-05-08

03:52:03--------------------------------------------[click]Ontheotherhand,arethegenerated

detailsrealdetails?

[click][click]CNN-basedSRmethodsincorporate

externaldata.Usingonlyoneframe,theycanalsoproducesharpstructures.Inthis

example,ontheright-hand-side,oneSRmethodgeneratessomeclearwindowpatternsonthebuilding,[click]buttheyarefarfromrealonthe

left.Theproblemis,detailsfromexternal

data,maynotbetrueforinputimage.MeritsinCoarse-to-fineStrategyEachscalesolvethesame

problemSolverandparametersateachscaleareusuallythe

sameOur MethodSolverSolver演示者2019-05-08

03:52:03--------------------------------------------Thegoalsofourmethodareasfollows.

[click]Wearetryingtomakebetteruse

of

sub-pixelmotionbetweenframesandproducehigh-qualityresultswithrealdetails.

[click]Wealsohopethedesignedframework

befullyscalable,intermsofinputimagesize,scalefactorsandframenumber.

[click]81Scale-recurrent

NetworkOur Method????3????2inputSolver????3SolverSolver????2????1????1EBlocksDBlocksSolverconvResBlockResBlockResBlockResBlockEBlocksResBloc

ResBlock kDBlocksdeconv演示者2019-05-08

03:52:03--------------------------------------------Thegoalsofourmethodareasfollows.

[click]Wearetryingtomakebetteruse

of

sub-pixelmotionbetweenframesandproducehigh-qualityresultswithrealdetails.

[click]Wealsohopethedesignedframework

befullyscalable,intermsofinputimagesize,scalefactorsandframenumber.

[click]8286DatafromGOPRO

dataset演示者2019-05-08

03:52:03--------------------------------------------Hereisonevideo

example.Characters,numbersandtextures

arehardtorecognizeinbicubicresult.Andoursresultsaremuchbetterand

clearer.UsingDifferentNumberof

ScalesAnalysis1

scaleInput2

scales3

scales84演示者2019-05-08

03:52:04--------------------------------------------Wedoanalysistoevaluateourmethod.

[click]First,areourrecovereddetailsreal?

[click]Hereweusethreeidenticalframes

asinputtoour

network.Theinformationcontainedinthisinput

isnomorethanonesinglelow-resimage.

[click]Asexpected,althoughsharper,the

outputcontainsnomoredetails.Andthecharactersandlogoarestill

unrecognizable.Baseline

ModelsAnalysisModelSSSCw/o

RRNNSR-FlatParam2.73M8.19M2.73M3.03M2.66MPSNR28.4029.0529.2629.3527.53Solver????1????1EBlocksDBlocksSolverSingleScale

(SS)85演示者2019-05-08

03:52:04--------------------------------------------[click]However,ifwetake3consecutive

framesfromthevideoasinput.

[click]Ournetworkproducesmuchbetter

results.Charactersandlogoareverycleartobe

read.Thisexperimentprovesthatthesharp

structuresrecoveredcomefromrealinformationofinputs,ratherthenfromexternalinformationinthenetwork.WewillbesafetotrusttheSR

results.Baseline

ModelsAnalysisModelSSSCw/o

RRNNSR-FlatParam2.73M8.19M2.73M3.03M2.66MPSNR28.4029.0529.2629.3527.53EBlocksDBlocksSolverScaleCascaded

(SC)????3????2Solver1????3Solver

2Solver

3????2????1????1演示者2019-05-08

03:52:04--------------------------------------------[click]However,ifwetake3consecutive

framesf