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Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory

Author

Listed:
  • Chia-Hsiang Lin

    (National Cheng Kung University
    National Cheng Kung University)

  • Shih-Hsiu Huang

    (National Cheng Kung University)

  • Ting-Hsuan Lin

    (National Cheng Kung University)

  • Pin Chieh Wu

    (National Cheng Kung University
    National Cheng Kung University)

Abstract

Hyperspectral imaging is vital for material identification but traditional systems are bulky, hindering the development of compact systems. While previous metasurfaces address volume issues, the requirements of complicated fabrication processes and significant footprint still limit their applications. This work reports a compact snapshot hyperspectral imager by incorporating the meta-optics with a small-data convex/deep (CODE) deep learning theory. Our snapshot hyperspectral imager comprises only one single multi-wavelength metasurface chip working in the visible window (500-650 nm), significantly reducing the device area. To demonstrate the high performance of our hyperspectral imager, a 4-band multispectral imaging dataset is used as the input. Through the CODE-driven imaging system, it efficiently generates an 18-band hyperspectral data cube with high fidelity using only 18 training data points. We expect the elegant integration of multi-resonant metasurfaces with small-data learning theory will enable low-profile advanced instruments for fundamental science studies and real-world applications.

Suggested Citation

  • Chia-Hsiang Lin & Shih-Hsiu Huang & Ting-Hsuan Lin & Pin Chieh Wu, 2023. "Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42381-5
    DOI: 10.1038/s41467-023-42381-5
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    References listed on IDEAS

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    1. Xiujuan Zou & Youming Zhang & Ruoyu Lin & Guangxing Gong & Shuming Wang & Shining Zhu & Zhenlin Wang, 2022. "Pixel-level Bayer-type colour router based on metasurfaces," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    2. Shuming Wang & Pin Chieh Wu & Vin-Cent Su & Yi-Chieh Lai & Cheng Hung Chu & Jia-Wern Chen & Shen-Hung Lu & Ji Chen & Beibei Xu & Chieh-Hsiung Kuan & Tao Li & Shining Zhu & Din Ping Tsai, 2017. "Broadband achromatic optical metasurface devices," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
    3. M. Saad Bin-Alam & Orad Reshef & Yaryna Mamchur & M. Zahirul Alam & Graham Carlow & Jeremy Upham & Brian T. Sullivan & Jean-Michel Ménard & Mikko J. Huttunen & Robert W. Boyd & Ksenia Dolgaleva, 2021. "Ultra-high-Q resonances in plasmonic metasurfaces," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    4. Xavier Hadoux & Flora Hui & Jeremiah K. H. Lim & Colin L. Masters & Alice Pébay & Sophie Chevalier & Jason Ha & Samantha Loi & Christopher J. Fowler & Christopher Rowe & Victor L. Villemagne & Edward , 2019. "Non-invasive in vivo hyperspectral imaging of the retina for potential biomarker use in Alzheimer’s disease," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
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    Cited by:

    1. Shih-Hsiu Huang & Hsiu-Ping Su & Chao-Yun Chen & Yu-Chun Lin & Zijin Yang & Yuzhi Shi & Qinghua Song & Pin Chieh Wu, 2024. "Microcavity-assisted multi-resonant metasurfaces enabling versatile wavefront engineering," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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