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

arXiv cs.LG / 4/17/2026

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Key Points

The paper proposes machine-learning methods to classify structured light beams after they acquire random speckle disturbances while propagating through turbulent media.
Beam propagation is simulated using a stochastic paraxial equation, and the authors tailor convolutional neural networks specifically for the resulting data representation.
They build a classification model using one-hot encoding and introduce a prediction-based generative diffusion model to augment training data when labeled samples are limited.
During training, the use of Bregman-distance minimization is reported to improve the quality of generated high-frequency modes, particularly for finer spectral features.

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.