Decoding Orbital Angular Momentum in Turbid Tissue-like Scattering Medium via Fourier-Domain Deep Learning

TL;DR

This paper presents VortexNet, a deep learning model that uses Fourier-domain analysis to accurately decode the orbital angular momentum of light beams in turbid, scattering media, enhancing optical communication and biomedical imaging.

Contribution

Introduction of VortexNet, a novel deep learning architecture that employs angular Fourier transforms to robustly decode OAM in scattering environments, surpassing classical methods.

Findings

VortexNet accurately classifies OAM in turbid media.

Angular Fourier Transform isolates persistent azimuthal features.

OAM correlations survive multiple scattering.

Abstract

Structured light beams carrying orbital angular momentum (OAM), such as Laguerre-Gaussian modes, are promising tools for high-capacity optical communications and advanced biomedical imaging. However, multiple scattering in turbid media distorts their phase and amplitude, complicating the retrieval of topological charge. We introduce VortexNet, a deep learning architecture that integrates an Angular Fourier Transform to explicitly extract rotational symmetries of OAM beams from experimentally acquired intensity and interference patterns. By transforming spatial information into the angular frequency domain, VortexNet isolates azimuthal features that persist despite scattering, enabling accurate topological charge classification even in complex optical environments. The results reveal that OAM-specific angular correlations can survive multiple scattering and be decoded through angular-domain learning. This establishes a new paradigm for structured-light analysis in complex medium, where deep learning enables the recovery of topological information beyond the reach of classical optics, paving the way for resilient photonic systems in communication, sensing, and imaging.