基于深度学习的多奇异结构光识别
Deep-learning-based recognition of multi-singularity structured light
摘要：
具有定制复杂拓扑图案的结构光激发了以精确检测技术为基础的各种经典和量子研究。然而，目前的检测方案仅限于具有简单相位奇异性的涡旋光束。对具有多个奇点的一般结构光的精确识别仍然是一个难题。在这里，我们报告了一个深度学习（DL）框架，该框架可以在通过相机捕获的光束传播时仅输入两个强度模式后，以端到端的方式揭示多奇点相位结构，从而释放扭曲光子的直观信息。DL工具箱还可以获取具有单奇点的拉盖尔-高斯（LG）模式的相位和其他一般相位对象。利用这个DL平台，基于相位的光学秘密共享（OSS）协议，这是基于一个更一般的类的多奇异模式比传统的LG光束。OSS协议具有安全性强、状态空间丰富、强度测量方便等特点。这项研究为涡旋光束通信、激光模式分析、显微镜、玻色-爱因斯坦凝聚体表征等开辟了新的途径。
英文摘要：
Structured light with customized complex topological pattern inspires diverse classical and quantum investigations underpinned by accurate detection techniques. However, the current detection schemes are limited to vortex beam with simple phase singularity. The precise recognition of general structured light with multiple singularities remains elusive. Here, we report a deep learning (DL) framework that can unveil multi-singularity phase structures in an end-to-end manner after feeding only two intensity patterns upon beam propagation captured via a camera, thus unleashing intuitive information of twisted photons. The DL toolbox can also acquire phases of Laguerre-Gaussian (LG) modes with single singularity and other general phase objects likewise. Leveraging this DL platform, a phase-based optical secret sharing (OSS) protocol is proposed, which is based on a more general class of multi-singularity modes than conventional LG beams. The OSS protocol features strong security, wealthy state space and convenient intensity-based measurements. This study opens new avenues for vortex beam communications, laser mode analysis, microscopy, Bose-Einstein condensates characterization, etc.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
