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Dual-Channel Switchable Metasurface Filters for Compact Spectral Imaging with Deep Compressive Reconstruction

Spectral imaging technology, which aims to capture images across multiple spectral channels and create a spectral data cube, has been widely utilized in various fields. However, conventional spectral imaging systems face challenges, such as slow acquisition speed and large size. The rapid developmen...

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Autores principales: Wang, Chang, Liu, Xinyu, Zhang, Yang, Sun, Yan, Yu, Zeqing, Zheng, Zhenrong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10649194/
https://www.ncbi.nlm.nih.gov/pubmed/37947699
http://dx.doi.org/10.3390/nano13212854
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author Wang, Chang
Liu, Xinyu
Zhang, Yang
Sun, Yan
Yu, Zeqing
Zheng, Zhenrong
author_facet Wang, Chang
Liu, Xinyu
Zhang, Yang
Sun, Yan
Yu, Zeqing
Zheng, Zhenrong
author_sort Wang, Chang
collection PubMed
description Spectral imaging technology, which aims to capture images across multiple spectral channels and create a spectral data cube, has been widely utilized in various fields. However, conventional spectral imaging systems face challenges, such as slow acquisition speed and large size. The rapid development of optical metasurfaces, capable of manipulating light fields versatilely and miniaturizing optical components into ultrathin planar devices, offers a promising solution for compact hyperspectral imaging (HSI). This study proposes a compact snapshot compressive spectral imaging (SCSI) system by leveraging the spectral modulations of metasurfaces with dual-channel switchable metasurface filters and employing a deep-learning-based reconstruction algorithm. To achieve compactness, the proposed system integrates dual-channel switchable metasurface filters using twisted nematic liquid crystals (TNLCs) and anisotropic titanium dioxide (TiO(2)) nanostructures. These thin metasurface filters are closely attached to the image sensor, resulting in a compact system. The TNLCs possess a broadband linear polarization conversion ability, enabling the rapid switching of the incidence polarization state between x-polarization and y-polarization by applying different voltages. This polarization conversion facilitates the generation of two groups of transmittance spectra for wavelength-encoding, providing richer information for spectral data cube reconstruction compared to that of other snapshot compressive spectral imaging techniques. In addition, instead of employing classic iterative compressive sensing (CS) algorithms, an end-to-end residual neural network (ResNet) is utilized to reconstruct the spectral data cube. This neural network leverages the 2-frame snapshot measurements of orthogonal polarization channels. The proposed hyperspectral imaging technology demonstrates superior reconstruction quality and speed compared to those of the traditional compressive hyperspectral image recovery methods. As a result, it is expected that this technology will have substantial implications in various domains, including but not limited to object detection, face recognition, food safety, biomedical imaging, agriculture surveillance, and so on.
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spelling pubmed-106491942023-10-27 Dual-Channel Switchable Metasurface Filters for Compact Spectral Imaging with Deep Compressive Reconstruction Wang, Chang Liu, Xinyu Zhang, Yang Sun, Yan Yu, Zeqing Zheng, Zhenrong Nanomaterials (Basel) Article Spectral imaging technology, which aims to capture images across multiple spectral channels and create a spectral data cube, has been widely utilized in various fields. However, conventional spectral imaging systems face challenges, such as slow acquisition speed and large size. The rapid development of optical metasurfaces, capable of manipulating light fields versatilely and miniaturizing optical components into ultrathin planar devices, offers a promising solution for compact hyperspectral imaging (HSI). This study proposes a compact snapshot compressive spectral imaging (SCSI) system by leveraging the spectral modulations of metasurfaces with dual-channel switchable metasurface filters and employing a deep-learning-based reconstruction algorithm. To achieve compactness, the proposed system integrates dual-channel switchable metasurface filters using twisted nematic liquid crystals (TNLCs) and anisotropic titanium dioxide (TiO(2)) nanostructures. These thin metasurface filters are closely attached to the image sensor, resulting in a compact system. The TNLCs possess a broadband linear polarization conversion ability, enabling the rapid switching of the incidence polarization state between x-polarization and y-polarization by applying different voltages. This polarization conversion facilitates the generation of two groups of transmittance spectra for wavelength-encoding, providing richer information for spectral data cube reconstruction compared to that of other snapshot compressive spectral imaging techniques. In addition, instead of employing classic iterative compressive sensing (CS) algorithms, an end-to-end residual neural network (ResNet) is utilized to reconstruct the spectral data cube. This neural network leverages the 2-frame snapshot measurements of orthogonal polarization channels. The proposed hyperspectral imaging technology demonstrates superior reconstruction quality and speed compared to those of the traditional compressive hyperspectral image recovery methods. As a result, it is expected that this technology will have substantial implications in various domains, including but not limited to object detection, face recognition, food safety, biomedical imaging, agriculture surveillance, and so on. MDPI 2023-10-27 /pmc/articles/PMC10649194/ /pubmed/37947699 http://dx.doi.org/10.3390/nano13212854 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Chang
Liu, Xinyu
Zhang, Yang
Sun, Yan
Yu, Zeqing
Zheng, Zhenrong
Dual-Channel Switchable Metasurface Filters for Compact Spectral Imaging with Deep Compressive Reconstruction
title Dual-Channel Switchable Metasurface Filters for Compact Spectral Imaging with Deep Compressive Reconstruction
title_full Dual-Channel Switchable Metasurface Filters for Compact Spectral Imaging with Deep Compressive Reconstruction
title_fullStr Dual-Channel Switchable Metasurface Filters for Compact Spectral Imaging with Deep Compressive Reconstruction
title_full_unstemmed Dual-Channel Switchable Metasurface Filters for Compact Spectral Imaging with Deep Compressive Reconstruction
title_short Dual-Channel Switchable Metasurface Filters for Compact Spectral Imaging with Deep Compressive Reconstruction
title_sort dual-channel switchable metasurface filters for compact spectral imaging with deep compressive reconstruction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10649194/
https://www.ncbi.nlm.nih.gov/pubmed/37947699
http://dx.doi.org/10.3390/nano13212854
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