Computational metaoptics for imaging
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917837320224768 |
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| author | Roques-Carmes, Charles Wang, Kai Yang, Yuanmu Majumdar, Arka Lin, Zin |
| author_facet | Roques-Carmes, Charles Wang, Kai Yang, Yuanmu Majumdar, Arka Lin, Zin |
| contents | 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 limitations of traditional imaging systems. This review explores the synergistic integration of metaoptics and computational imaging, "computational metaoptics," which combines the physical wavefront shaping ability of metasurfaces with advanced computational algorithms to enhance imaging performance beyond conventional limits. We discuss how computational metaoptics addresses the inherent limitations of single-layer metasurfaces in achieving multifunctionality without compromising efficiency. By treating metasurfaces as physical preconditioners and co-designing them with reconstruction algorithms through end-to-end (inverse) design, it is possible to jointly optimize the optical hardware and computational software. This holistic approach allows for the automatic discovery of optimal metasurface designs and reconstruction methods that significantly improve imaging capabilities. Advanced applications enabled by computational metaoptics are highlighted, including phase imaging and quantum state measurement, which benefit from the metasurfaces' ability to manipulate complex light fields and the computational algorithms' capacity to reconstruct high-dimensional information. We also examine performance evaluation challenges, emphasizing the need for new metrics that account for the combined optical and computational nature of these systems. Finally, we identify new frontiers in computational metaoptics which point toward a future where computational metaoptics may play a central role in advancing imaging science and technology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09133 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computational metaoptics for imaging Roques-Carmes, Charles Wang, Kai Yang, Yuanmu Majumdar, Arka Lin, Zin Optics Computer Vision and Pattern Recognition Computational Physics Quantum Physics 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 limitations of traditional imaging systems. This review explores the synergistic integration of metaoptics and computational imaging, "computational metaoptics," which combines the physical wavefront shaping ability of metasurfaces with advanced computational algorithms to enhance imaging performance beyond conventional limits. We discuss how computational metaoptics addresses the inherent limitations of single-layer metasurfaces in achieving multifunctionality without compromising efficiency. By treating metasurfaces as physical preconditioners and co-designing them with reconstruction algorithms through end-to-end (inverse) design, it is possible to jointly optimize the optical hardware and computational software. This holistic approach allows for the automatic discovery of optimal metasurface designs and reconstruction methods that significantly improve imaging capabilities. Advanced applications enabled by computational metaoptics are highlighted, including phase imaging and quantum state measurement, which benefit from the metasurfaces' ability to manipulate complex light fields and the computational algorithms' capacity to reconstruct high-dimensional information. We also examine performance evaluation challenges, emphasizing the need for new metrics that account for the combined optical and computational nature of these systems. Finally, we identify new frontiers in computational metaoptics which point toward a future where computational metaoptics may play a central role in advancing imaging science and technology. |
| title | Computational metaoptics for imaging |
| topic | Optics Computer Vision and Pattern Recognition Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2411.09133 |