Motion Deblurring. Algorithms and Systems

309 Pages·2014·13.407 MB·English

by  A.N. Rajagopalan, Rama Chellappa (eds.)

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Motion Deblurring provides a comprehensive guide to restoring images degraded by
motionblur,bridgingtraditionalapproachesandemergingcomputationalphotography-
basedtechniques,andbringingtogetherawiderangeofmethodsemergingfrombasic
theory and cutting-edge research. It encompasses both algorithms and architectures,
providingdetailedcoverageofpracticaltechniquesbyleadingresearchers.
From an algorithms perspective, blind and non-blind approaches are discussed,
including the use of single or multiple images, projective motion blur models, image
priors and parametric models, high dynamic range imaging in the irradiance domain,
and image recognition in blur. Performance limits for motion deblurring cameras are
alsopresented.
Fromasystemsperspective,hybridframeworkscombininglowresolutionhigh-speed
andhighresolutionlow-speedcamerasarecovered,alongwiththeuseofinertialsensors
andcodedexposurecameras.Anarchitectureexploitingcompressivesensingforvideo
recoveryisalsodescribed.
This book will be a valuable resource for researchers and practitioners in computer
vision,imageprocessing,andrelatedfields.
A.N.RAJAGOPALANisaProfessorintheDepartmentofElectricalEngineeringattheIndian
InstituteofTechnology,Madras.Heco-authoredthebookDepthFromDefocus:AReal
Aperture Imaging Approach in 1998. He is a Fellow of the Alexander von Humboldt
Foundation, Germany, Fellow of the Indian National Academy of Engineering, and a
SeniorMemberoftheIEEE.HereceivedtheOutstandingInvestigatorAwardfromthe
DepartmentofAtomicEnergy,India,in2012andtheVASVIKawardin2013.
RAMACHELLAPPA is Minta Martin Professor of Engineering and an affiliate Professor of
ComputerScienceatUniversityofMaryland,CollegePark.Heisalsoaffiliatedwiththe
CenterforAutomationResearchandUMIACS,andisservingastheChairoftheECE
department.HeisarecipientoftheK.S.FuPrizefromIAPRandtheSociety,Technical
AchievementandMeritoriousServiceAwardsfromtheIEEESignalProcessingSociety.
HealsoreceivedtheTechnicalAchievementandMeritoriousServiceAwardsfromthe
IEEEComputerSociety.In2010,hewasrecognizedasanOutstandingECEbyPurdue
University. He is a Fellow of IEEE, IAPR, OSA and AAAS, a Golden Core Member
oftheIEEEComputerSociety,andhasservedasaDistinguishedLectureroftheIEEE
SignalProcessingSociety,andasthePresidentofIEEEBiometricsCouncil.
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Motion Deblurring
Algorithms and Systems
Editedby
A.N. RAJAGOPALAN
IndianInstituteofTechnology,Madras
RAMA CHELLAPPA
UniversityofMaryland,CollegePark
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UniversityPrintingHouse,CambridgeCB28BS,UnitedKingdom
PublishedintheUnitedStatesofAmericabyCambridgeUniversityPress,NewYork
CambridgeUniversityPressispartoftheUniversityofCambridge.
ItfurtherstheUniversity’smissionbydisseminatingknowledgeinthepursuitof
education,learningandresearchatthehighestinternationallevelsofexcellence.
www.cambridge.org
Informationonthistitle:www.cambridge.org/9781107044364
©CambridgeUniversityPress2014
Thispublicationisincopyright.Subjecttostatutoryexception
andtotheprovisionsofrelevantcollectivelicensingagreements,
noreproductionofanypartmaytakeplacewithoutthewritten
permissionofCambridgeUniversityPress.
Firstpublished2014
PrintedintheUnitedKingdombyXXXXXXXXXXXXXXXXXXXXXXY
AcatalogrecordforthispublicationisavailablefromtheBritishLibrary
LibraryofCongressCataloguinginPublicationData
ISBN 978-1-107-04436-4Hardback
CambridgeUniversityPresshasnoresponsibilityforthepersistenceoraccuracyof
URLsforexternalorthird-partyinternetwebsitesreferredtointhispublication,
anddoesnotguaranteethatanycontentonsuchwebsitesis,orwillremain,
accurateorappropriate.
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Contents
Listofcontributors pageix
Preface xi
1 Mathematicalmodelsandpracticalsolversforuniformmotiondeblurring 1
1.1 Non-blinddeconvolution 1
1.2 Blinddeconvolution 12
2 Spatiallyvaryingimagedeblurring 31
2.1 Reviewofimagedeblurringmethods 31
2.2 Aunifiedcamerashakeblurmodel 32
2.3 Singleimagedeblurringusingmotiondensityfunctions 35
2.4 Imagedeblurringusinginertialmeasurementsensors 36
2.5 Generatingsharppanoramasfrommotion-blurredvideos 46
2.6 Discussion 54
3 Hybrid-imagingformotiondeblurring 57
3.1 Introduction 57
3.2 Fundamentalresolutiontradeoff 57
3.3 Hybrid-imagingsystems 59
3.4 ShiftinvariantPSFimagedeblurring 61
3.5 Spatially-varyingPSFimagedeblurring 67
3.6 Movingobjectdeblurring 70
3.7 Discussionandsummary 72
4 Efficient,blind,spatially-variantdeblurringforshakenimages 75
4.1 Introduction 75
4.2 Modellingspatially-variantcamerashakeblur 76
4.3 Thecomputationalmodel 82
4.4 Blindestimationofblurfromasingleimage 83
4.5 Efficientcomputationofthespatially-variantmodel 87
4.6 Single-imagedeblurringresults 94
4.7 Implementation 95
4.8 Conclusion 97
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vi Contents
5 Removingcamerashakeinsmartphoneswithouthardwarestabilization 100
5.1 Introduction 100
5.2 Imageacquisitionmodel 100
5.3 Inverseproblem 102
5.4 Pinholecameramodel 109
5.5 Smartphoneapplication 112
5.6 Evaluation 117
5.7 Conclusions 119
6 Multi-sensorfusionformotiondeblurring 123
6.1 Introduction 123
6.2 Hybrid-speedsensor 124
6.3 Motiondeblurring 125
6.4 Depthmapsuper-resolution 128
6.5 Extensionstolow-lightimaging 132
6.6 Discussionandsummary 137
7 Motiondeblurringusingflutteredshutter 141
7.1 Relatedwork 141
7.2 Codedexposurephotography 142
7.3 Imagedeconvolution 142
7.4 Codeselection 144
7.5 Linearsolutionfordeblurring 147
7.6 Resolutionenhancement 150
7.7 OptimizedcodesforPSFestimation 151
7.8 Implementation 156
7.9 Analysis 157
7.10 Summary 159
8 Richardson–Lucydeblurringforscenesunderaprojectivemotionpath 161
8.1 Introduction 161
8.2 Relatedwork 163
8.3 Theprojectivemotionblurmodel 164
8.4 ProjectivemotionRichardson–Lucy 165
8.5 Motionestimation 170
8.6 Experimentresults 171
8.7 Discussionandconclusion 179
9 HDRimaginginthepresenceofmotionblur 184
9.1 Introduction 184
9.2 ExistingapproachestoHDRI 186
9.3 CRF,irradianceestimationandtone-mapping 189
9.4 HDRimagingunderuniformblurring 191
9.5 HDRIfornon-uniformblurring 192
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Contents vii
9.6 Experimentalresults 199
9.7 Conclusionsanddiscussions 203
10 Compressivevideosensingtotacklemotionblur 207
10.1 Introduction 207
10.2 Relatedwork 208
10.3 Imagingarchitecture 211
10.4 High-speedvideorecovery 213
10.5 Experimentalresults 216
10.6 Conclusions 219
11 Codedexposuremotiondeblurringforrecognition 222
11.1 Motionsensitivityofirisrecognition 223
11.2 Codedexposure 225
11.3 Codedexposureperformanceonirisrecognition 239
11.4 Barcodes 241
11.5 Moregeneralsubjectmotion 241
11.6 Implicationsofcomputationalimagingforrecognition 242
11.7 Conclusion 244
12 Directrecognitionofmotion-blurredfaces 246
12.1 Introduction 246
12.2 Thesetofallmotion-blurredimages 249
12.3 Bankofclassifiersapproachforrecognizingmotion-blurredfaces 251
12.4 Experimentalevaluation 252
12.5 Discussion 255
13 Performancelimitsformotiondeblurringcameras 258
13.1 Introduction 258
13.2 Performanceboundsforfluttershuttercameras 261
13.3 Performanceboundformotion-invariantcameras 265
13.4 Simulationstoverifyperformancebounds 269
13.5 Roleofimagepriors 270
13.6 Whentousecomputationalimaging 273
13.7 Relationshiptoothercomputationalimagingsystems 274
13.8 Summaryanddiscussion 278
Index 283
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List of contributors
AmitAgrawal
MitsubishiElectricResearchLabs(MERL),USA
RajagopalanN.Ambasamudram
IndianInstituteofTechnologyMadras,India
MosheBen-Ezra
MassachusettsInstituteofTechnology,USA
MichaelS.Brown
NationalUniversityofSingapore,Singapore
ParamanandChandramouli
IndianInstituteofTechnologyMadras,India
VijayS.Channarayapatna
IndianInstituteofTechnologyMadras,India
RamaChellappa
UniversityofMaryland,USA
OliverCossairt
NorthwesternUniversity,USA
JanFlusser
CzechAcademyofSciences,CzechRepublic
MohitGupta
MitsubishiElectricResearchLabs(MERL),USA
JiayaJia
TheChineseUniversityofHongKong
NeelJoshi
MicrosoftResearch,USA
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