End-to-end metasurface inverse design for single-shot multi-channel imaging
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Abstract (arXiv)
We introduce end-to-end metaoptics inverse design for multi-channel imaging: reconstruction of depth, spectral and polarization channels from a single-shot monochrome image. The proposed technique integrates a single-layer metasurface frontend with an efficient Tikhonov reconstruction backend, without any additional optics except a grayscale sensor. Our method yields multi-channel imaging by spontaneous demultiplexing: the metaoptics front-end separates different channels into distinct spatial domains whose locations on the sensor are optimally discovered by the inverse-design algorithm. We present large-area metasurface designs, compatible with standard lithography, for multi-spectral imaging, depth-spectral imaging, and ``all-in-one'' spectro-polarimetric-depth imaging with robust reconstruction performance (lesssim 10% error with 1% detector noise). In contrast to neural networks, our framework is physically interpretable and does not require large training sets. It can be used to reconstruct arbitrary three-dimensional scenes with full multi-wavelength spectra and polarization textures.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Nov 2021.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.
5 papers.
- 2023Revealing the preference for correcting separated aberrations in joint optic-image designcited by 6
- 2025HiLAB: A Hybrid Inverse-Design Frameworkcited by 6
- 2023Photonic Structures Optimization Using Highly Data-Efficient Deep Learning: Application To Nanofin And Annular Groove Phase Maskscited by 0
- 2024Computational metaoptics for imagingcited by 0
- 2026VLM-Aware Meta-Optic Front-End Design for Frozen Vision-Language Modelscited by 0
- 2026VLM-Aware Meta-Optic Front-End Design for Frozen Vision-Language Models26 Jun 2026
- 2025HiLAB: A Hybrid Inverse-Design Framework23 May 2025
- 2024Computational metaoptics for imaging14 Nov 2024
- 2023Revealing the preference for correcting separated aberrations in joint optic-image design8 Sep 2023
- 2023Photonic Structures Optimization Using Highly Data-Efficient Deep Learning: Application To Nanofin And Annular Groove Phase Masks5 Sep 2023