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

arXiv: https://arxiv.org/abs/2312.02669 · HTML: https://mlsanity.com/paper/2312.02669/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2023.

## Built on by (most cited first, 6 of 6)

- [Enabling High-Quality In-the-Wild Imaging from Severely Aberrated Metalens Bursts](https://mlsanity.com/paper/2510.10083/) (2025)
- [MOCLIP: A Foundation Model for Large-Scale Nanophotonic Inverse Design](https://mlsanity.com/paper/2511.18980/) (2025)
- [MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality](https://mlsanity.com/paper/2605.29212/) (2026)
- [Generalizing Deep Surrogate Solvers for Broadband Electromagnetic Field Prediction at Unseen Wavelengths](https://mlsanity.com/paper/2408.02971/) (2024)
- [MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy](https://mlsanity.com/paper/2508.03596/) (2025)
- [VLM-Aware Meta-Optic Front-End Design for Frozen Vision-Language Models](https://mlsanity.com/paper/2606.27646/) (2026)

## Abstract (arXiv)

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.