Low cost hyperspectral imaging using deep learning based spectral reconstruction

Julius Tschannerl, Jinchang Ren, Stephen Marshall

Research output: Contribution to conferenceAbstractpeer-review

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Abstract

The increasing number of applications of hyperspectral imaging results in a high demand for low cost, mobile devices. We propose a multispectral imaging (MSI) system based on time-multiplexed lighting using RGB Light Emitting Diodes (LED). We train a deep neural network that maps low dimensional multispectral input onto high dimensional hyperspectral (HSI) output that is collected with a HSI camera covering the range of 400 – 950 nm. Results on the 24 colour patches of the Macbeth colour checker chart show that with only five multispectral bands, a very accurate reconstruction of HSI data can be achieved.
Original languageEnglish
Number of pages2
Publication statusPublished - 10 Oct 2018
EventHyperspectral Imaging Applications (HSI) 2018 -
Duration: 10 Oct 201811 Oct 2018
https://www.hsi2018.com

Conference

ConferenceHyperspectral Imaging Applications (HSI) 2018
Period10/10/1811/10/18
Internet address

Keywords

  • hyperspectral imaging
  • deep learning
  • spectral reconstruction
  • LED illumination

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