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.
Creators(s): |
Tschannerl, Julius ![]() ![]() ![]() | Item type: | Conference or Workshop Item(Other) |
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ID code: | 65968 |
Keywords: | hyperspectral imaging, deep learning, spectral reconstruction, LED illumination, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering |
Subjects: | Technology > Electrical engineering. Electronics Nuclear engineering |
Department: | Faculty of Engineering > Electronic and Electrical Engineering Strategic Research Themes > Measurement Science and Enabling Technologies |
Depositing user: | Pure Administrator |
Date deposited: | 02 Nov 2018 15:04 |
Last modified: | 17 Dec 2020 03:52 |
Related URLs: | |
URI: | https://strathprints.strath.ac.uk/id/eprint/65968 |
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