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240409s2024 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202313357
|2 doi
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|a pubmed24n1432.xml
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|a DE-627
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a He, Haoyang
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Meta-Attention Network Based Spectral Reconstruction with Snapshot Near-Infrared Metasurface
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| 264 |
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|c 2024
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 07.06.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2024 Wiley‐VCH GmbH.
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|a Near-infrared (NIR) spectral information is important for detecting and analyzing material compositions. However, snapshot NIR spectral imaging systems still pose significant challenges owing to the lack of high-performance NIR filters and bulky setups, preventing effective encoding and integration with mobile devices. This study introduces a snapshot spectral imaging system that employs a compact NIR metasurface featuring 25 distinct C4 symmetry structures. Benefitting from the sufficient spectral variety and low correlation coefficient among these structures, center-wavelength accuracy of 0.05 nm and full width at half maximum accuracy of 0.13 nm are realized. The system maintains good performance within an incident angle of 1°. A novel meta-attention network prior iterative denoising reconstruction (MAN-IDR) algorithm is developed to achieve high-quality NIR spectral imaging. By leveraging the designed metasurface and MAN-IDR, the NIR spectral images, exhibiting precise textures, minimal artifacts in the spatial dimension, and little crosstalk between spectral channels, are reconstructed from a single grayscale recording image. The proposed NIR metasurface and MAN-IDR hold great promise for further integration with smartphones and drones, guaranteeing the adoption of NIR spectral imaging in real-world scenarios such as aerospace, health diagnostics, and machine vision
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|a Journal Article
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|a attention guided network
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| 650 |
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|a near‐infrared metasurface
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| 650 |
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4 |
|a snapshot spectral imaging
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| 700 |
1 |
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|a Zhang, Yuzhe
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shao, Yujie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Yan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Geng, Guangzhou
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Junjie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Xin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Yongtian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bian, Liheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Jun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Lingling
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 36(2024), 23 vom: 03. Juni, Seite e2313357
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
1 |
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|g volume:36
|g year:2024
|g number:23
|g day:03
|g month:06
|g pages:e2313357
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| 856 |
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|u http://dx.doi.org/10.1002/adma.202313357
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