Neural nano-optics for high-quality thin lens imaging
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 alter...
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Published in: | Nature communications Vol. 12; no. 1; p. 6493 |
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Main Authors: | , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
London
Nature Publishing Group UK
29-11-2021
Nature Publishing Group Nature Portfolio |
Subjects: | |
Online Access: | Get full text |
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Summary: | 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.
While meta-optics have the potential to dramatically miniaturize camera technology, the quality of the captured images remains poor. Co-designing a single meta-optic and software correction, here the authors report on full-color imaging with quality comparable to commercial cameras. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-021-26443-0 |