Filter Selection for Hyperspectral Estimation

Boaz Arad, Ohad Ben-Shahar

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


While recovery of hyperspectral signals from natural RGB images has been a recent subject of exploration, little to no consideration has been given to the camera response profiles used in the recovery process. In this paper we demonstrate that optimal selection of camera response filters may improve hyperspectral estimation accuracy by over 33%, emphasizing the importance of considering and selecting these response profiles wisely. Additionally, we present an evolutionary optimization methodology for optimal filter set selection from very large filter spaces, an approach that facilitates practical selection from families of customizable filters or filter optimization for multispectral cameras with more than 3 channels.

Original languageAmerican English
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
Number of pages9
ISBN (Electronic)9781538610329
StatePublished - 22 Dec 2017
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: 22 Oct 201729 Oct 2017

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


Conference16th IEEE International Conference on Computer Vision, ICCV 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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