ML-based approach to classification and generation of structured light propagation in turbulent media

TL;DR

This paper introduces machine learning techniques, including CNNs and diffusion models, to classify and generate structured light propagation patterns in turbulent atmospheres, enhancing understanding and analysis.

Contribution

It presents a novel ML framework combining classification and generative models tailored for structured light in turbulence, with improved data augmentation methods.

Findings

CNNs effectively classify structured light beams in turbulence.

Diffusion models generate high-quality data to augment limited datasets.

Bregman distance minimization enhances high-frequency mode generation.

Abstract

This work develops machine learning approaches to classify structured light wave beams developing random speckle disturbances as they propagate through turbulent atmospheres. Beam propagation is modeled by the numerical simulation of a stochastic paraxial equation. We design convolutional neural networks tailored for this specific application and use them for a classification model with one-hot encoding. To address the challenge of potentially limited available data, we develop a prediction-based generative diffusion model to provide additional data during classifier training. We show that a Bregman distance minimization during the learning step improves the quality of the generation of high-frequency modes.