ML-based approach to classification and generation of structured light propagation in turbulent media
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...
| Authors | Aokun Wang, Anjali Nair, Zhongjian Wang et al. |
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| Primary category | physics.optics |
| Published | 4 April 2026 |
| Last updated at source | 8 July 2026 |
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