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

Authors

Aokun Wang, Anjali Nair, Zhongjian Wang, Guillaume Bal

Published in

Journal of the Optical Society of America. A, Optics, image science, and vision. Volume 43. Issue 7. Pages C61-C70. Jul 01, 2026.

Abstract

We study the classification task of structured light beams after propagation through a random turbulent medium. The received speckle patterns are generated by numerical simulation of a stochastic paraxial propagation model, and the classification task is formulated over a finite alphabet of 15 OAM source classes. We benchmark intensity and autocorrelation inputs using SimpleCNN and ResNet-18 as classifiers. We also quantify the effect of training-set size and receiver-window misalignment. Since additional propagated samples may be costly to obtain, we develop a class-conditioned diffusion model for generative augmentation of turbulence-degraded intensity images. The main contribution is a spectrum-aware diffusion objective: a pixel-domain loss combined with a Fourier-domain Bregman regularizer designed to preserve high-frequency speckle statistics. We prove that this hybrid objective is consistent with the posterior-mean regression target of the diffusion model and show that generated samples substantially improve low-data classification.

PMID:
42863036
Bibliographic data and abstract were imported from PubMed on 13 Oct 2026.

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