Sea-THRU: A method for removing water from underwater images

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Robust recovery of lost colors in underwater images remains a challenging problem. We recently showed that this was partly due to the prevalent use of an atmospheric image formation model for underwater images. We proposed a physically accurate model that explicitly showed: 1)~the attenuation coefficient of the signal is not uniform across the scene but depends on object range and reflectance, 2)~the coefficient governing the increase in backscatter with distance differs from the signal attenuation coefficient. Here, we present a method that recovers color with the revised model using RGBD images. The emph{Sea-thru} method first calculates backscatter using the darkest pixels in the image and their known range information. Then, it uses an estimate of the spatially varying illuminant to obtain the range-dependent attenuation coefficient. Using more than 1,100 images from two optically different water bodies, which we make available, we show that our method outperforms those using the atmospheric model. Consistent removal of water will open up large underwater datasets to powerful computer vision and machine learning algorithms, creating exciting opportunities for the future of underwater exploration and conservation.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
PublisherIEEE Computer Society
Pages1682-1691
Number of pages10
ISBN (Electronic)9781728132938
DOIs
StatePublished - Jun 2019
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States
Duration: 16 Jun 201920 Jun 2019

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2019-June
ISSN (Print)1063-6919

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
Country/TerritoryUnited States
CityLong Beach
Period16/06/1920/06/19

Bibliographical note

Funding Information:
This work was supported by the The Leona M. and Harry B. Helmsley Charitable Trust, the Maurice Hatter Foundation, Ministry of Science, Technology and Space grant #3 − 12487, ISF grant #680/18, the Technion Ollendorff Minerva Center for Vision and Image Sciences, the University of Haifa institutional postdoctoral program. We thank Tom Shlesinger, Deborah Levy, Matan Yuval, Ben Singer, H. Can Karaimer, and the Interuniversity Institute of Marine Sciences in Eilat.

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Computational Photography
  • Low-level Vision

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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