Fang, F., Yang, M., Wang, H., Liang, X., Mass, T., Xu, X., Yang, X., & Li, Z. (2026). Next-Generation Metalens Vision System: Powered by AI and Applied to AI. Proceedings of the AAAI Conference on Artificial Intelligence, 40(48), 41580–41582. https://doi.org/10.1609/aaai.v40i48.42346
Abstract:
Metalenses have been widely recognized as a key building block of next-generation optical systems, offering unprecedented advantages in compactness, lightweight design, and scalable manufacturing compared to traditional refractive optics. Despite this promise, practical use is limited by optical aberrations, blur, and illumination sensitivity, which degrade both visual quality and machine perception. In this demonstration, we present an end-to-end metalens vision system—from hardware sensing with a custom-built RGB metalens camera, to physics-informed imaging and real-time restoration, and finally to downstream vision applications such as object detection and depth estimation. By integrating spatially-aware attention enhancement and reinforcement learning-based illumination control into a real-time system, our solution transforms degraded raw captures into high-fidelity images that are both visually interpretable and functionally reliable for machine vision. This AI-powered pipeline highlights metalenses as a cornerstone for next-generation imaging, where advances in optics and machine intelligence jointly drive the future of visual perception.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - GAP project
Grant Reference no. : I24D1AG062
This research / project is supported by the A*STAR - Manufacturing, Trade, and Connectivity Programmatic Fund
Grant Reference no. : M23L7b0021