Liquid Crystal Metasurface Enabled Hyperspectral Single-pixel Imaging

Liquid Crystal Metasurface Enabled Hyperspectral Single-pixel Imaging | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Liquid Crystal Metasurface Enabled Hyperspectral Single-pixel Imaging Jiewen Nie, Jiewen Nie, Tao Chen, Shan Jiang, Peng Dai, Pivnenko Mike, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8004352/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Near-infrared (NIR) hyperspectral imaging emerges as a versatile, non-destructive method for material characterisation by acquiring spatial-spectral data cubes. Yet, conventional implementations demand costly InGaAs focal-plane arrays and scanning assemblies (e.g., narrowband or slit scanners), resulting in prohibitively high cost and complexity. Here, we firstly propose a transmissive liquid crystal metasurface (LC-MS) for NIR spectral compression, which enables a computational hyperspectral single-pixel imaging (HS-SPI) system. Our streamlined design integrates a voltage-controlled LC-MS on a bucket detector within an SLM-based single-pixel imaging system, enabling spatio-spectral joint encoding and detection. The spectral image is then reconstructed by a generative deep learning model with high noise robustness. Our validation experiment presents 64×64 pixel resolution hyperspectral imaging over 261 bands (1500–1630 nm, 0.5 nm sampling), achieving a 66:1 spatio-spectral compression ratio with sub-nanometer wavelength accuracy (~0.8nm). The voltage-controlled transmissive LC-MS device facilitates seamless integration in both electronic control and optomechanical design. As an initial proof-of-concept, this approach exhibits substantial potential for further optimisation in device design and system performance, ultimately promoting cost-effective portable spectral imaging systems for transformative applications in remote sensing, field-deployable diagnostics, and mobile monitoring. Physical sciences/Optics and photonics/Applied optics/Mid-infrared photonics Physical sciences/Optics and photonics/Optical techniques/Imaging and sensing Physical sciences/Optics and photonics/Optical materials and structures/Metamaterials Physical sciences/Physics/Electronics, photonics and device physics/Photonic devices Full Text Additional Declarations There is no conflict of interest Supplementary Files supplementary.pdf SUPPLEMENTARY INFORMATION Liquid Crystal Metasurface Enabled Hyperspectral Single-pixel Imaging Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8004352","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":545209678,"identity":"ca5d565e-6665-4cbb-bc0a-4be5032d86ea","order_by":0,"name":"Jiewen 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