Neural nano-optics for high-quality thin lens imaging

Ethan Tseng, Shane Colburn, James Whitehead, Luocheng Huang, Seung-Hwan Baek, Arka Majumdar and Felix Heide ()
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Ethan Tseng: Department of Computer Science
Shane Colburn: Department of Electrical and Computer Engineering
James Whitehead: Department of Electrical and Computer Engineering
Luocheng Huang: Department of Electrical and Computer Engineering
Seung-Hwan Baek: Department of Computer Science
Arka Majumdar: Department of Electrical and Computer Engineering
Felix Heide: Department of Computer Science

Nature Communications, 2021, vol. 12, issue 1, 1-7

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26443-0

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DOI: 10.1038/s41467-021-26443-0

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