Defocus Deblurring and Superresolution for Time-of-Flight Depth Cameras

Handle URI:
http://hdl.handle.net/10754/556519
Title:
Defocus Deblurring and Superresolution for Time-of-Flight Depth Cameras
Authors:
Xiao, Lei; Heide, Felix; O'Toole, Matthew; Kolb, Andreas; Hullin, Matthias B.; Kutulakos, Kyros; Heidrich, Wolfgang ( 0000-0002-4227-8508 )
Abstract:
Continuous-wave time-of-flight (ToF) cameras show great promise as low-cost depth image sensors in mobile applications. However, they also suffer from several challenges, including limited illumination intensity, which mandates the use of large numerical aperture lenses, and thus results in a shallow depth of field, making it difficult to capture scenes with large variations in depth. Another shortcoming is the limited spatial resolution of currently available ToF sensors. In this paper we analyze the image formation model for blurred ToF images. By directly working with raw sensor measurements but regularizing the recovered depth and amplitude images, we are able to simultaneously deblur and super-resolve the output of ToF cameras. Our method outperforms existing methods on both synthetic and real datasets. In the future our algorithm should extend easily to cameras that do not follow the cosine model of continuous-wave sensors, as well as to multi-frequency or multi-phase imaging employed in more recent ToF cameras.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
IEEE
Journal:
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
Conference/Event name:
Computer Vision and Pattern Recognition (CVPR),2015
Issue Date:
7-Jun-2015
Type:
Conference Paper
Additional Links:
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiao_Defocus_Deblurring_and_2015_CVPR_paper.pdf
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorXiao, Leien
dc.contributor.authorHeide, Felixen
dc.contributor.authorO'Toole, Matthewen
dc.contributor.authorKolb, Andreasen
dc.contributor.authorHullin, Matthias B.en
dc.contributor.authorKutulakos, Kyrosen
dc.contributor.authorHeidrich, Wolfgangen
dc.date.accessioned2015-06-07T22:08:29Zen
dc.date.available2015-06-07T22:08:29Zen
dc.date.issued2015-06-07en
dc.identifier.urihttp://hdl.handle.net/10754/556519en
dc.description.abstractContinuous-wave time-of-flight (ToF) cameras show great promise as low-cost depth image sensors in mobile applications. However, they also suffer from several challenges, including limited illumination intensity, which mandates the use of large numerical aperture lenses, and thus results in a shallow depth of field, making it difficult to capture scenes with large variations in depth. Another shortcoming is the limited spatial resolution of currently available ToF sensors. In this paper we analyze the image formation model for blurred ToF images. By directly working with raw sensor measurements but regularizing the recovered depth and amplitude images, we are able to simultaneously deblur and super-resolve the output of ToF cameras. Our method outperforms existing methods on both synthetic and real datasets. In the future our algorithm should extend easily to cameras that do not follow the cosine model of continuous-wave sensors, as well as to multi-frequency or multi-phase imaging employed in more recent ToF cameras.en
dc.publisherIEEEen
dc.relation.urlhttp://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiao_Defocus_Deblurring_and_2015_CVPR_paper.pdfen
dc.rightsThese CVPR 2015 papers are the Open Access versions, provided by the Computer Vision Foundation. The authoritative versions of these papers are posted on IEEE Xplore.en
dc.titleDefocus Deblurring and Superresolution for Time-of-Flight Depth Camerasen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015en
dc.conference.dateJune 7th-12th, 2015en
dc.conference.nameComputer Vision and Pattern Recognition (CVPR),2015en
dc.conference.locationBoston, Massachusettsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionUniversity of British Columbiaen
dc.contributor.institutionUniversity of Torontoen
dc.contributor.institutionUniversity of Siegenen
dc.contributor.institutionUniversity of Bonnen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.