Spectral reconstruction with model-based neural network for liquid crystal modulator devices

Jizhou Zhang; Tingfa Xu; Qingwang Qin; Yuhan Zhang

doi:10.1117/12.2604353

1 November 2021 Spectral reconstruction with model-based neural network for liquid crystal modulator devices

Jizhou Zhang, Tingfa Xu, Qingwang Qin, Yuhan Zhang

Proceedings Volume 12057, Twelfth International Conference on Information Optics and Photonics; 120570U (2021) https://doi.org/10.1117/12.2604353
Event: Twelfth International Conference on Information Optics and Photonics, 2021, Xi'an, China

Abstract

The liquid crystal modulator devices (LCMD) have become an important technique in the field of hyperspectral imaging. However, the spectral resolution and accuracy of LCMD-based imaging spectrometers are limited due to their principle. To break this limitation and promote the application of LCMD, we propose a spectral reconstruction method using model-based neural networks. The calibrated spectral transmittance of LCMD and a carefully designed loss function are used to constraint the calculation. Experiments on reconstructing both substance spectra and spectral image cubes have validated the effectiveness and superiority of the proposed method.

Citation Download Citation

Jizhou Zhang, Tingfa Xu, Qingwang Qin, and Yuhan Zhang "Spectral reconstruction with model-based neural network for liquid crystal modulator devices", Proc. SPIE 12057, Twelfth International Conference on Information Optics and Photonics, 120570U (1 November 2021); https://doi.org/10.1117/12.2604353

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KEYWORDS

Neural networks

Transmittance

Liquid crystals

Model-based design

Spectral resolution

Polarizers

Wave plates

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