Compact hyperspectral imaging of extreme-depth-of-field diffractive lenses based on spatial–spectral sparse deep learning

Compact hyperspectral imaging of extreme-depth-of-field diffractive lenses based on spatial–spectral sparse deep learning | 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 Compact hyperspectral imaging of extreme-depth-of-field diffractive lenses based on spatial–spectral sparse deep learning Chaoyang Wei, Zhenqi Niu, Yuying Lu, Songlin Wan, Jian-Da Shao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3651437/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 Thin and flat diffractive optical elements (DOEs) are significant in the field of integrated optics and provide a novel and optimal solution for hyperspectral imaging (HI) which is expected to be compact, snapshot, with large depth of field (DoF) and resolution. The tradeoff between spectral and spatial resolutions caused by the restricted DoF, limits the application scenarios for HI. To address this, based on the prior of spatial and spectral sparse, we propose a spatial–spectral achromatic (SSA) neural network to end-to-end optimize a broad-bandwidth system with a DOE to provide the support for snapshotly achromatic extreme-DoF HI. We experimentally show that our system can snapshotly capture achromatic, high-fidelity hyperspectral images with 25 spectral channels ranging from 420 nm to 660 nm, covering distances from 0.5 m to 5 m. The proposed system enables precise and dynamic reconstruction of spectra within an extreme DoF, a capability previously unattainable with compact computational spectral cameras. The precise reconstruction of spectra demonstrates the potential of the developed system in various applications, such as precision agriculture, food quality inspection, and object detection. Physical sciences/Optics and photonics/Optical techniques/Imaging and sensing Physical sciences/Physics/Optical physics/Micro-optics Physical sciences/Optics and photonics/Applied optics/Optical sensors Physical sciences/Optics and photonics/Applied optics/Integrated optics Full Text Additional Declarations (Not answered) Supplementary Files Supportinginformation.docx SupportinginformationFallingProcess.mp4 Falling Process 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. We do this by developing innovative software and high quality services for the global research community. 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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-3651437","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":254685446,"identity":"55ca4bd0-e9aa-406f-9d0b-782a79f65dfb","order_by":0,"name":"Chaoyang 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