Reconstructing Intensity Images from Binary Spatial Gradient Cameras

Suren Jayasuriya, Orazio Gallo, Jinwei Gu, Timo Aila, Jan Kautz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Binary gradient cameras extract edge and temporal information directly on the sensor, allowing for low-power, low-bandwidth, and high-dynamic-range capabilities - all critical factors for the deployment of embedded computer vision systems. However, these types of images require specialized computer vision algorithms and are not easy to interpret by a human observer. In this paper we propose to recover an intensity image from a single binary spatial gradient image with a deep auto-encoder. Extensive experimental results on both simulated and real data show the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
PublisherIEEE Computer Society
Pages337-343
Number of pages7
ISBN (Electronic)9781538607336
DOIs
StatePublished - Aug 22 2017
Event30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 - Honolulu, United States
Duration: Jul 21 2017Jul 26 2017

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2017-July
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Other

Other30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
CountryUnited States
CityHonolulu
Period7/21/177/26/17

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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