ML-based approach to classification and generation of structured light propagation in turbulent media
On the map: Diffusion models → Inverse problems and image restoration
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 9 of its 36 references that are in the map. First posted on arXiv 4 Apr 2026.
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9 of 36 references.
References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2018On the Spectral Bias of Neural Networkscited by 1,341
- 2015Cyclical Learning Rates for Training Neural Networkscited by 629
- 2020Focal Frequency Loss for Image Reconstruction and Synthesiscited by 206
- 2023Mathematical analysis of singularities in the diffusion model under the submanifold assumptioncited by 20
- 2025Pathway to O(√d) Complexity bound under Wasserstein metric of flow-based modelscited by 4
- 2020Structured Light in Turbulencecited by 2
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- 2023DiffuseRAW: End-to-End Generative RAW Image Processing for Low-Light Images
- 2026Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks
- 2025Preserving Spectral Structure and Statistics in Diffusion Models
- 2026Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration
- 2023Global Structure-Aware Diffusion Process for Low-Light Image Enhancement