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arXiv · 2102.11579

Neural Nano-Optics for High-quality Thin Lens Imaging

Ethan Tseng·Shane Colburn·James Whitehead·Luocheng Huang·Seung-Hwan Baek·Arka Majumdar·Felix Heide
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Abstract

Nano-optic imagers that modulate light at sub-wavelength scales could unlock unprecedented 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 presenting the first neural nano-optics. We devise a fully differentiable learning method that learns a metasurface physical structure in conjunction with a novel, neural feature-based image reconstruction algorithm. Experimentally validating the proposed method, we achieve an order of magnitude lower reconstruction error. As such, we present the first high-quality, nano-optic imager that combines the widest field of view for full-color metasurface operation while simultaneously achieving the largest demonstrated 0.5 mm, f/2 aperture.

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Ethan Tseng, Shane Colburn, James Whitehead, Luocheng Huang, Seung-Hwan Baek, Arka Majumdar, Felix Heide. 2021-02-23. Neural Nano-Optics for High-quality Thin Lens Imaging. https://doi.org/10.1038/s41467-021-26443-0

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