Speckle-Driven Single-Shot Orbital Angular Momentum Recognition with Ultra-Low Sampling Density

TL;DR

This paper introduces a speckle-driven method for recognizing orbital angular momentum of vortex beams using minimal sampling points, transforming scattering media into an efficient encoding mechanism for optical information processing.

Contribution

The authors propose SMPD, a novel speckle-based recognition technique that drastically reduces sampling requirements and works effectively in scattering media, unlike traditional high-resolution methods.

Findings

Achieves over 99% accuracy with only 16 sampling points.

Reduces sampling density to 0.024% compared to conventional methods.

Demonstrates versatility across various optical recognition tasks.

Abstract

Orbital angular momentum (OAM) recognition of vortex beams is critical for applications ranging from optical communications to quantum technologies. However, conventional approaches designed for free-space propagation struggle when light passes through scattering media, such as multimode fibers (MMF), and often rely on high-resolution sensors with tens of thousands of pixels to record detailed intensity profiles. Here, by harnessing scattering media as intrinsic encoders rather than detrimental factors, we introduce a speckle-driven OAM recognition technique termed patially multiplexed points detection (SMPD). This method extracts intensity information from a few spatially distributed points in a speckle plane, where object feature is naturally multiplexed, thereby transforming scattering from a detrimental effect into an efficient encoding mechanism while drastically reducing sampling requirements. Remarkably, it achieves over 99% retrieval accuracy for OAMs recognition using just 16 sampling points, corresponding to a sampling density of 0.024% compared with conventional imaging-based approaches. Furthermore, spatiotemporally interleaved vortex beams decoding, highcapacity OAM-multiplexed communication, MNIST, and Fashion-MNIST classification are implemented to verify the versatility of SMPD. This work establishes a scalable strategy for efficient optical information processing and fiberbased sensing in complex environments.