Hyperspectral image reconstruction using multi-colour and time-multiplexed LED illumination

Julius Tschannerl, Jinchang Ren, Huimin Zhao, Fu-Jen kao, Stephen Marshall, Peter Yuen

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
19 Downloads (Pure)


The rapidly rising industrial interest in hyperspectral imaging (HSI) has generated an increased demand for cost effective HSI devices. We are demonstrating a mobile and low-cost multispectral imaging system, enabled by time-multiplexed RGB Light Emitting Diodes (LED) illumination, which operates at video framerate. Critically, a deep Multi-Layer Perceptron (MLP) with HSI prior in the spectral range of 400–950 nm is trained to reconstruct HSI data. We incorporate regularisation and dropout to compensate for overfitting in the largely ill-posed problem of reconstructing the HSI data. The MLP is characterised by a relatively simple design and low computational burden. Experimental results on 51 objects of various references and naturally occurring materials show the effectiveness of this approach in terms of reconstruction error and classification accuracy. We were also able to show that utilising additional colour channels to the three R, G and B channels adds increased value to the reconstruction.
Original languageEnglish
Pages (from-to)352-357
Number of pages6
JournalOptics and Lasers in Engineering
Early online date6 May 2019
Publication statusPublished - 31 Oct 2019


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


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