ML-based approach to classification and generation of structured light propagation in turbulent media
For researchers in optical communications and atmospheric optics, this provides a method to classify and generate structured light in turbulence, but the improvements are incremental.
This work develops ML approaches to classify structured light beams in turbulent atmospheres, using CNNs and a generative diffusion model with Bregman distance minimization to improve classification accuracy with limited data.
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.