arXiv cs.AI· Rose Johnson, Debabrata Mandal, Zihao Zou, Jessie Wei, Anna Wirth-Singh, Vladimir Yarmolik, Johannes Froech, Arka Majumdar, Praneeth Chakravarthula·· 13 小时前AI 评分31
全硅双曲面超透镜实现 3-5 微米宽带中波红外成像
Broadband Mid-Wave Infrared Imaging Enabled by All-Silicon Hyperboloid Metalens
AI 导读
研究团队用单层硅超表面加计算重建,在完整 3-5 微米中波红外波段实现宽带成像,重建模型仅需四次图像采集即可完成适配。该方法采用直径 2cm 的简单双曲面超透镜,利用其波长相关响应编码的信息,无需设计宽带消色差超透镜。
正文 · 原文
Abstract:The mid-wave infrared (MWIR, 3-5 microns) is indispensable for thermal sensing, gas detection, long-range imaging, and free-space optical communications. Meta-optics offer a compact, lightweight, and potentially low-cost alternative to conventional refractive optics, making them attractive for size, weight, and power (SWaP)-constrained systems. However, their strong chromatic dispersion has largely restricted imaging to narrow spectral bands. Here, we demonstrate broadband imaging across the full 3-5 micron MWIR band using a single-layer silicon metasurface coupled with computational reconstruction. Remarkably, the reconstruction model is adapted using only four image captures, overcoming large training data requirements and enabling broadband image recovery despite the strongly wavelength-dependent optical response. In contrast to approaches that engineer complex, dispersion-optimized metalens profiles for broadband operation, our method leverages the wavelength-dependent information encoded by a simple 2cm diameter hyperboloid metalens. We show that this metasurface captures sufficient broadband intensity information for computational reconstruction across the entire MWIR band. These results establish a simple route toward broadband computational meta-optics without requiring broadband achromatic metalens designs, offering a promising platform for compact MWIR imaging systems.
| Subjects: | Optics (physics.optics); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.29841 [physics.optics] |
| (or arXiv:2610.29841v1 [physics.optics] for this version) | |
| https://doi.org/10.48550/arXiv.2610.29841 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Praneeth Chakravarthula [view email]
[v1]
Wed, 21 Oct 2026 15:04:30 UTC (4,457 KB)
来源:arXiv cs.AI · arxiv.org