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

TL;DR

This paper introduces a deep learning framework that enhances metalens imaging, enabling aberration-free, full-color, high-resolution images, overcoming traditional limitations in broadband metalenses for compact optical applications.

Contribution

It presents the first end-to-end deep learning approach for metalens imaging that significantly reduces aberrations and improves image quality in broadband metalenses.

Findings

Achieved aberration-free full-color imaging with 10-mm diameter metalenses.

Neural network-assisted imaging matches ground truth resolution.

Overcomes trade-offs between bandwidth and focusing efficiency.

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.