Deep-learning-driven end-to-end metalens imaging
Joonhyuk Seo, Jaegang Jo, Joohoon Kim, J.S. Kang, Chanik Kang, Seong‐Won Moon, Eunji Lee, Jehyeong Hong, Junsuk Rho, Hsiao L. Chung
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 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. 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.