Deep-learning-based recognition of multi-singularity structured light

TL;DR

This paper introduces a deep learning framework capable of recognizing complex multi-singularity structured light patterns from simple intensity measurements, enabling advanced applications in optical communication and microscopy.

Contribution

The authors develop an end-to-end deep learning method for recognizing multi-singularity phase structures from intensity patterns, extending detection capabilities beyond simple vortex beams.

Findings

Successfully recognized multi-singularity phase structures using only two intensity images.

Enabled phase retrieval of Laguerre-Gaussian modes and other phase objects.

Proposed a secure phase-based optical secret sharing protocol using multi-singularity modes.

Abstract

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.