Neural nano-optics for high-quality thin lens imaging
SCIE
SCOPUS
- Title
- Neural nano-optics for high-quality thin lens imaging
- Authors
- Tseng, Ethan; Colburn, Shane; Whitehead, James; Huang, Luocheng; Baek, Seung-Hwan; Majumdar, Arka; Heide, Felix
- Date Issued
- 2021-11
- Publisher
- Nature Publishing Group
- Abstract
- Nano-optic imagers that modulate light at sub-wavelength scales could enable new applications in diverse domains ranging from robotics to medicine. Although metasurface optics offer a path to such ultra-small imagers, existing methods have achieved image quality far worse than bulky refractive alternatives, fundamentally limited by aberrations at large apertures and low f-numbers. In this work, we close this performance gap by introducing a neural nano-optics imager. We devise a fully differentiable learning framework that learns a metasurface physical structure in conjunction with a neural feature-based image reconstruction algorithm. Experimentally validating the proposed method, we achieve an order of magnitude lower reconstruction error than existing approaches. As such, we present a high-quality, nano-optic imager that combines the widest field-of-view for full-color metasurface operation while simultaneously achieving the largest demonstrated aperture of 0.5 mm at an f-number of 2.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/109474
- DOI
- 10.1038/s41467-021-26443-0
- ISSN
- 2041-1723
- Article Type
- Article
- Citation
- Nature Communications, vol. 12, no. 1, 2021-11
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- There are no files associated with this item.
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