Deep-learning-driven end-to-end metalens imaging
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.
Cite
@article{arxiv.2312.02669,
title = {Deep-learning-driven end-to-end metalens imaging},
author = {Joonhyuk Seo and Jaegang Jo and Joohoon Kim and Joonho Kang and Chanik Kang and Seongwon Moon and Eunji Lee and Jehyeong Hong and Junsuk Rho and Haejun Chung},
journal= {arXiv preprint arXiv:2312.02669},
year = {2024}
}
Comments
17 pages, 7 figures, 1 table