Neural array meta-imaging

Xiong Dun; Jian Zhang; Fansheng Chen; Zhanyi Zhang; Xuquan Wang; Yujie Xing; Siyu Dong; Zeying Fan; Yuzhi Shi; Wetzstein Gordon; Zhanshan Wang; Xinbin Cheng

doi:10.1186/s43593-025-00107-8

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Neural array meta-imaging

eLight Vol. 5, (2025)

Research Article | Updated：2025-12-17

- Neural array meta-imaging
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- Author bio：
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- DOI：10.1186/s43593-025-00107-8
  CLC：
- Received：13 April 2025，
  
  Revised：2025-09-11，
  
  Accepted：17 September 2025，
  
  Published Online：01 December 2025，
  
  Published：2025-12
- Accepted：
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Xiong Dun, Jian Zhang, Fansheng Chen, et al. Neural array meta-imaging[J]. eLight, 2025, 5.
DOI：

Xiong Dun, Jian Zhang, Fansheng Chen, et al. Neural array meta-imaging[J]. eLight, 2025, 5. DOI： 10.1186/s43593-025-00107-8.

摘要

Abstract

Compact

high-quality imaging systems are highly desired for scientific

industrial

and consumer applications. Metalenses combined with computational imaging offer a promising solution for developing such systems

yet their performance is fundamentally limited by the commonly used point-to-point imaging model

which forces trade-offs between aperture size

F-number

field of view (FOV)

waveband width

and image quality. Here

we experimentally demonstrate that a neural array imaging model can overcome these long-standing trade-offs

achieving a 25-Hz full-color imaging camera with a 2.76-mm aperture

1.45 F-number

$$$^{\circ }$$$

FOV

and a spectral range of 400–700 nm. The camera achieves image quality comparable to commercial compound lenses (e.g.

Edmund 33-300) in both indoor and outdoor environments

while reducing the total track length by a factor of 13. We further demonstrate its suitability for object detection and depth estimation in real-world scenarios. This neural array imaging model is also applied to polarization imaging

showcasing its scalability and versatility for broadband applications.

关键词

Keywords

references

G. Iddan , G. Meron , A. Glukhovsky , P. Swain , Wireless capsule endoscopy . Nature 405 , 417 - 417 ( 2000 ). http://doi.org/10.1038/35013140 http://doi.org/10.1038/35013140

D.F. Hougen et al. , A miniature robotic system for reconnaissance and surveillance . EEE Int. Confer. Robot. Autom 1 , 501 - 507 ( 2000 ).

O. Cogal , Y. Leblebici , An insect eye inspired miniaturized multi-camera system for endoscopic imaging . IEEE Trans. Biomed. Circuits Syst. 11 , 212 - 224 ( 2016 ). http://doi.org/10.1109/TBCAS.2016.2547388 http://doi.org/10.1109/TBCAS.2016.2547388

M. Delbracio , D. Kelly , M.S. Brown , P. Milanfar , Mobile computational photography: a tour . Annu. Rev. Vis. Sci. 7 , 571 - 604 ( 2021 ). http://doi.org/10.1146/annurev-vision-093019-115521 http://doi.org/10.1146/annurev-vision-093019-115521

D.A.B. Miller , Why optics needs thickness . Science 379 , 41 - 45 ( 2023 ). http://doi.org/10.1126/science.ade3395 http://doi.org/10.1126/science.ade3395

M. Khorasaninejad , F. Capasso , Metalenses: versatile multifunctional photonic components . Science 358 ,( 2017 ). http://doi.org/10.1126/science.aam8100 http://doi.org/10.1126/science.aam8100

A. Arbabi , Y. Horie , M. Bagheri , A. Faraon , Dielectric metasurfaces for complete control of phase and polarization with subwavelength spatial resolution and high transmission . Nat. Nanotechnol. 10 , 937 - 943 ( 2015 ). http://doi.org/10.1038/nnano.2015.186 http://doi.org/10.1038/nnano.2015.186

J. Zuo et al. , Chip-integrated metasurface full-stokes polarimetric imaging sensor . Light Sci. Appl. 12 , 218 ( 2023 ). http://doi.org/10.1038/s41377-023-01260-w http://doi.org/10.1038/s41377-023-01260-w

M. Jang et al. , Wavefront shaping with disorder-engineered metasurfaces . Nat. Photon. 12 , 84 - 90 ( 2018 ). http://doi.org/10.1038/s41566-017-0078-z http://doi.org/10.1038/s41566-017-0078-z

B. Xiong et al. , Breaking the limitation of polarization multiplexing in optical metasurfaces with engineered noise . Science 379 , 294 - 299 ( 2023 ). http://doi.org/10.1126/science.ade5140 http://doi.org/10.1126/science.ade5140

J.E. Fröch et al. , Real time full-color imaging in a meta-optical fiber endoscope . eLight 3 ,( 2023 ). http://doi.org/10.1186/s43593-023-00044-4 http://doi.org/10.1186/s43593-023-00044-4

H. Huang et al. , Leaky-wave metasurfaces for integrated photonics . Nat. Nanotechnol. 18 , 580 - 588 ( 2023 ). http://doi.org/10.1038/s41565-023-01360-z http://doi.org/10.1038/s41565-023-01360-z

F. Presutti , F. Monticone , Focusing on bandwidth: achromatic metalens limits . Optica 7 , 624 - 631 ( 2020 ). http://doi.org/10.1364/OPTICA.389404 http://doi.org/10.1364/OPTICA.389404

S. Wang et al. , A broadband achromatic metalens in the visible . Nat. Nanotechnol. 13 , 227 - 232 ( 2018 ). http://doi.org/10.1038/s41565-017-0052-4 http://doi.org/10.1038/s41565-017-0052-4

W.T. Chen et al. , A broadband achromatic metalens for focusing and imaging in the visible . Nat. Nanotechnol. 13 , 220 - 226 ( 2018 ). http://doi.org/10.1038/s41565-017-0034-6 http://doi.org/10.1038/s41565-017-0034-6

Y. Wang et al. , High-efficiency broadband achromatic metalens for near-IR biological imaging window . Nat. Commun. 12 ,( 2021 ). http://doi.org/10.1038/s41467-021-25797-9 http://doi.org/10.1038/s41467-021-25797-9

C.-F. Pan et al. , 3d-printed multilayer structures for high–numerical aperture achromatic metalenses . Sci. Adv. 9 ,( 2023 ). http://doi.org/10.1126/sciadv.adj9262 http://doi.org/10.1126/sciadv.adj9262

J. Engelberg , U. Levy , Achromatic flat lens performance limits . Optica 8 , 834 - 845 ( 2021 ). http://doi.org/10.1364/OPTICA.422843 http://doi.org/10.1364/OPTICA.422843

S. Chang et al. , Achromatic metalenses for full visible spectrum with extended group delay control via dispersion-matched layers . Nat. Commun. 15 ,( 2024 ). http://doi.org/10.1038/s41467-024-53701-8 http://doi.org/10.1038/s41467-024-53701-8

Z. Zhang et al. , Enhancing photon throughput of miniaturized passive depth-detection cameras via broadband dispersion-engineered metalenses . ACS Photon. 10 , 3789 - 3796 ( 2023 ). http://doi.org/10.1021/acsphotonics.3c01078 http://doi.org/10.1021/acsphotonics.3c01078

L. Huang , S. Colburn , A. Zhan , A. Majumdar , Full-color metaoptical imaging in visible light . Adv. Photon. Res. 3 ,( 2022 ). http://doi.org/10.1002/adpr.202100265 http://doi.org/10.1002/adpr.202100265

M.Y. Shalaginov et al. , Single-element diffraction-limited fisheye metalens . Nano Lett. 20 , 7429 - 7437 ( 2020 ). http://doi.org/10.1021/acs.nanolett.0c02783 http://doi.org/10.1021/acs.nanolett.0c02783

A. Martins et al. , On metalenses with arbitrarily wide field of view . ACS Photon. 7 , 2073 - 2079 ( 2020 ). http://doi.org/10.1021/acsphotonics.0c00479 http://doi.org/10.1021/acsphotonics.0c00479

T. Xie et al. , Ultrathin, wide-angle, and high-resolution meta-imaging system via rear-position wavevector filter . Laser Photon. Rev. 17 , 2300119 ( 2023 ). http://doi.org/10.1002/lpor.202300119 http://doi.org/10.1002/lpor.202300119

G. Wetzstein et al. , Inference in artificial intelligence with deep optics and photonics . Nature 588 , 39 - 47 ( 2020 ). http://doi.org/10.1038/s41586-020-2973-6 http://doi.org/10.1038/s41586-020-2973-6

G. Barbastathis , A. Ozcan , G. Situ , On the use of deep learning for computational imaging . Optica 6 , 921 - 943 ( 2019 ). http://doi.org/10.1364/OPTICA.6.000921 http://doi.org/10.1364/OPTICA.6.000921

Y. Luo et al. , Computational imaging without a computer: seeing through random diffusers at the speed of light . eLight 2 ,( 2022 ). http://doi.org/10.1186/s43593-022-00012-4 http://doi.org/10.1186/s43593-022-00012-4

J. Wu et al. , An integrated imaging sensor for aberration-corrected 3d photography . Nature 612 , 62 - 71 ( 2022 ). http://doi.org/10.1038/s41586-022-05306-8 http://doi.org/10.1038/s41586-022-05306-8

S. Colburn , A. Zhan , A. Majumdar , Metasurface optics for full-color computational imaging . Sci. Adv. 4 ,( 2018 ). http://doi.org/10.1126/sciadv.aar2114 http://doi.org/10.1126/sciadv.aar2114

E. Bayati et al. , Inverse designed extended depth of focus meta-optics for broadband imaging in the visible . Nanophotonics 11 , 2531 - 2540 ( 2021 ). http://doi.org/10.1515/nanoph-2021-0431 http://doi.org/10.1515/nanoph-2021-0431

E. Tseng et al. , Neural nano-optics for high-quality thin lens imaging . Nat. Commun. 12 ,( 2021 ). http://doi.org/10.1038/s41467-021-26443-0 http://doi.org/10.1038/s41467-021-26443-0

V. Sitzmann et al. , End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging . ACM Trans. Graph. 37 , 1 - 13 ( 2018 ). http://doi.org/10.1145/3197517.3201333 http://doi.org/10.1145/3197517.3201333

J.E. Fröch et al. , Beating spectral bandwidth limits for large aperture broadband nano-optics . Nat. Commun. 16 ,( 2025 ). http://doi.org/10.1038/s41467-025-58208-4 http://doi.org/10.1038/s41467-025-58208-4

R. Maman , E. Mualem , N. Mazurski , J. Engelberg , U. Levy , Achromatic imaging systems with flat lenses enabled by deep learning . ACS Photon. 10 , 4494 - 4500 ( 2023 ). http://doi.org/10.1021/acsphotonics.3c01349 http://doi.org/10.1021/acsphotonics.3c01349

J. Seo et al. , Deep-learning-driven end-to-end metalens imaging . Adv. Photon. 6 , 066002 - 066002 ( 2024 ). http://doi.org/10.1117/1.AP.6.6.066002 http://doi.org/10.1117/1.AP.6.6.066002

P. Chakravarthula et al. , Thin on-sensor nanophotonic array cameras . ACM Trans. Graph. 42 , 1 - 18 ( 2023 ). http://doi.org/10.1145/3618398 http://doi.org/10.1145/3618398

J.H. Hammond, The camera obscura: a chronicle (Adam Hilger Ltd, Bristol 1981)

J.W. Goodman, Introduction to fourier optics, 3rd edn. (Hoddesdon, 2005)

R.J. Lin et al. , Achromatic metalens array for full-colour light-field imaging . Nat. Nanotechnol. 14 , 227 - 231 ( 2019 ). http://doi.org/10.1038/s41565-018-0347-0 http://doi.org/10.1038/s41565-018-0347-0

Q. Fan et al. , Trilobite-inspired neural nanophotonic light-field camera with extreme depth-of-field . Nat. Commun. 13 ,( 2022 ). http://doi.org/10.1038/s41467-022-29568-y http://doi.org/10.1038/s41467-022-29568-y

A.L. Holsteen , D. Lin , I. Kauvar , G. Wetzstein , M.L. Brongersma , A light-field metasurface for high-resolution single-particle tracking . Nano Lett. 19 , 2267 - 2271 ( 2019 ). http://doi.org/10.1021/acs.nanolett.8b04673 http://doi.org/10.1021/acs.nanolett.8b04673

J. Dong , S. Roth , B. Schiele , Deep wiener deconvolution: wiener meets deep learning for image deblurring . Adv. Neural. Inf. Process. Syst. 33 , 1048 - 1059 ( 2020 ).

L. Chen, X. Chu, X. Zhang, J. Sun, Simple baselines for image restoration. Proceedings of the European conference on computer vision (ECCV) 17–33 (2022)

Y. Zhang, et al. Image super-resolution using very deep residual channel attention networks. Proceedings of the European conference on computer vision (ECCV) 286–301 (2018)

K. Yanny , K. Monakhova , R.W. Shuai , L. Waller , Deep learning for fast spatially varying deconvolution . Optica 9 , 96 - 99 ( 2022 ). http://doi.org/10.1364/OPTICA.442438 http://doi.org/10.1364/OPTICA.442438

J. Wu , V. Boominathan , A. Veeraraghavan , J.T. Robinson , Real-time, deep-learning aided lensless microscope . Biomed. Opt. Express 14 , 4037 - 4051 ( 2023 ). http://doi.org/10.1364/BOE.490199 http://doi.org/10.1364/BOE.490199

L. Denis , E. Thiébaut , F. Soulez , J.-M. Becker , R. Mourya , Fast approximations of shift-variant blur . Int. J. Comput. Vision 115 , 253 - 278 ( 2015 ). http://doi.org/10.1007/s11263-015-0817-x http://doi.org/10.1007/s11263-015-0817-x

L. Yang et al. , Depth anything v2 . Adv. Neural. Inf. Process. Syst. 37 , 21875 - 21911 ( 2025 ).

G. Jocher, et al. ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation. Zenodo (2022)

K. Wei et al. , Spatially varying nano-neural networks . Sci. Adv. 10 ,( 2024 ). http://doi.org/10.1126/sciadv.adp0391 http://doi.org/10.1126/sciadv.adp0391

E. Arbabi , S.M. Kamali , A. Arbabi , A. Faraon , Full-stokes imaging polarimetry using dielectric metasurfaces . ACS Photon. 5 , 3132 - 3140 ( 2018 ). http://doi.org/10.1021/acsphotonics.8b00362 http://doi.org/10.1021/acsphotonics.8b00362

N.A. Rubin et al. , Matrix fourier optics enables a compact full-stokes polarization camera . Science 365 ,( 2019 ). http://doi.org/10.1126/science.aax1839 http://doi.org/10.1126/science.aax1839

J.P. Hugonin, P. Lalanne, Reticolo software for grating analysis. arXiv preprint arXiv:2101.00901 http://arxiv.org/abs/2101.00901 (2021)

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