Sparse recovery of hyperspectral signal from natural RGB images

Boaz Arad, Ohad Ben-Shahar

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

173 Scopus citations

Abstract

Hyperspectral imaging is an important visual modality with growing interest and range of applications. The latter, however, is hindered by the fact that existing devices are limited in either spatial, spectral, and/or temporal resolution, while yet being both complicated and expensive. We present a low cost and fast method to recover high quality hyperspectral images directly from RGB. Our approach first leverages hyperspectral prior in order to create a sparse dictionary of hyperspectral signatures and their corresponding RGB projections. Describing novel RGB images via the latter then facilitates reconstruction of the hyperspectral image via the former. A novel, larger-than-ever database of hyperspectral images serves as a hyperspectral prior. This database further allows for evaluation of our methodology at an unprecedented scale, and is provided for the benefit of the research community. Our approach is fast, accurate, and provides high resolution hyperspectral cubes despite using RGB-only input.

Original languageEnglish
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
PublisherSpringer Verlag
Pages19-34
Number of pages16
ISBN (Print)9783319464770
DOIs
StatePublished - 1 Jan 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9911 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th European Conference on Computer Vision, ECCV 2016
Country/TerritoryNetherlands
CityAmsterdam
Period8/10/1616/10/16

Fingerprint

Dive into the research topics of 'Sparse recovery of hyperspectral signal from natural RGB images'. Together they form a unique fingerprint.

Cite this