Low cost hyperspectral imaging using deep learning based spectral reconstruction

Tschannerl, Julius and Ren, Jinchang and Marshall, Stephen (2018) Low cost hyperspectral imaging using deep learning based spectral reconstruction. In: Hyperspectral Imaging Applications (HSI) 2018, 2018-10-10 - 2018-10-11.

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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.

ORCID iDs

Tschannerl, Julius ORCID logoORCID: https://orcid.org/0000-0002-4613-1693, Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194 and Marshall, Stephen ORCID logoORCID: https://orcid.org/0000-0001-7079-5628;
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