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dc.contributor.authorXiao, Lei
dc.contributor.authorHeide, Felix
dc.contributor.authorO'Toole, Matthew
dc.contributor.authorKolb, Andreas
dc.contributor.authorHullin, Matthias B.
dc.contributor.authorKutulakos, Kyros
dc.contributor.authorHeidrich, Wolfgang
dc.identifier.citationLei Xiao, Heide, F., O’Toole, M., Kolb, A., Hullin, M. B., Kutulakos, K., & Heidrich, W. (2015). Defocus deblurring and superresolution for time-of-flight depth cameras. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2015.7298851
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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
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.
dc.titleDefocus Deblurring and Superresolution for Time-of-Flight Depth Cameras
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
dc.conference.dateJune 7th-12th, 2015
dc.conference.nameComputer Vision and Pattern Recognition (CVPR),2015
dc.conference.locationBoston, Massachusetts
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUniversity of British Columbia
dc.contributor.institutionUniversity of Toronto
dc.contributor.institutionUniversity of Siegen
dc.contributor.institutionUniversity of Bonn

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