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arXiv · 2312.02669

Deep-learning-driven end-to-end metalens imaging

Joonhyuk Seo·Jaegang Jo·Joohoon Kim·Joonho Kang·Chanik Kang·Seongwon Moon·Eunji Lee·Jehyeong Hong·Junsuk Rho·Haejun Chung
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Abstract

Recent advances in metasurface lenses (metalenses) have shown great potential for opening a new era in compact imaging, photography, light detection and ranging (LiDAR), and virtual reality/augmented reality (VR/AR) applications. However, the fundamental trade-off between broadband focusing efficiency and operating bandwidth limits the performance of broadband metalenses, resulting in chromatic aberration, angular aberration, and a relatively low efficiency. In this study, a deep-learning-based image restoration framework is proposed to overcome these limitations and realize end-to-end metalens imaging, thereby achieving aberration-free full-color imaging for mass-produced metalenses with 10-mm diameter. Neural-network-assisted metalens imaging achieved a high resolution comparable to that of the ground truth image.

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Joonhyuk Seo, Jaegang Jo, Joohoon Kim, Joonho Kang, Chanik Kang, Seongwon Moon, Eunji Lee, Jehyeong Hong, Junsuk Rho, Haejun Chung. 2023-12-05. Deep-learning-driven end-to-end metalens imaging. https://arxiv.org/abs/2312.02669

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