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Neural nano-optics for high-quality thin lens imaging SCIE SCOPUS

Title
Neural nano-optics for high-quality thin lens imaging
Authors
Tseng, EthanColburn, ShaneWhitehead, JamesHuang, LuochengBaek, Seung-HwanMajumdar, ArkaHeide, 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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백승환BAEK, SEUNG HWAN
Dept of Computer Science & Enginrg
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