Metaoptic Computational Imaging

Metasurfaces, ultrathin structures composed of subwavelength optical elements, have revolutionized light manipulation by enabling precise control over electromagnetic waves’ amplitude, phase, polarization, and spectral properties. Concurrently, computational imaging leverages algorithms to reconstruct images from optically processed signals, overcoming the limitations of traditional imaging systems. This Perspective explores the synergistic integration of metaoptics and computational imaging, “metaoptic computational imaging”, which combines the physical wavefront shaping ability of metasurfaces with advanced computational algorithms to enhance imaging performance beyond conventional limits. We discuss how metaoptic computational imaging addresses the inherent limitations of single-layer metasurfaces in achieving multifunctionality without compromising efficiency. By treating metasurfaces as physical preconditioners and codesigning them with reconstruction algorithms through end-to-end (inverse) design, it is possible to jointly optimize the optical hardware and computational software. Advanced applications and new frontiers in the field enabled by metaoptic computational imaging are highlighted, including phase imaging and quantum state measurement.

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Publication Details

Journal
ACS Photonics
Published
2025-02-13
DOI
https://doi.org/10.1021/acsphotonics.4c02266
Citations
8
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
19.85
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article

Metaoptic Computational Imaging

Arka Majumdar, Charles Roques‐Carmes, Yuanmu Yang, Kai Wang et al.
8 citations
ACS Photonics
Neural Networks and Reservoir Computing
19.85
article

Metaoptic Computational Imaging

Arka Majumdar, Charles Roques‐Carmes, Yuanmu Yang, Kai Wang, Zin Lin
article en
8 citations

Abstract

Metasurfaces, ultrathin structures composed of subwavelength optical elements, have revolutionized light manipulation by enabling precise control over electromagnetic waves’ amplitude, phase, polarization, and spectral properties. Concurrently, computational imaging leverages algorithms to reconstruct images from optically processed signals, overcoming the limitations of traditional imaging systems. This Perspective explores the synergistic integration of metaoptics and computational imaging, “metaoptic computational imaging”, which combines the physical wavefront shaping ability of metasurfaces with advanced computational algorithms to enhance imaging performance beyond conventional limits. We discuss how metaoptic computational imaging addresses the inherent limitations of single-layer metasurfaces in achieving multifunctionality without compromising efficiency. By treating metasurfaces as physical preconditioners and codesigning them with reconstruction algorithms through end-to-end (inverse) design, it is possible to jointly optimize the optical hardware and computational software. Advanced applications and new frontiers in the field enabled by metaoptic computational imaging are highlighted, including phase imaging and quantum state measurement.

ACS PhotonicsVol. 12(4)
University of Washington (US), Bradley University (US), Research Institute of Precision Instruments (Russia) (RU), McGill University (CA), Virginia Tech (US), Stanford University (US), Tsinghua University (CN)
Openalex Percentile: Top 1%
Neural Networks and Reservoir Computing
19.85
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Metaoptic Computational Imaging — Arka Majumdar, Charles Roques‐Carmes, et al. · ACS Photonics (2025) | TGRS Research Map | TGRS