用扩散模型生成湍流退化的光场图像，提升小样本分类性能
ML-based approach to classification and generation of structured light propagation in turbulent media
- 基于频谱感知损失的扩散模型生成湍流退化图像
- 在仅150张训练图时分类准确率达89.2%
- 适合光学通信与弱信号识别研究者
研究结构光束经随机湍流介质传播后的分类任务。接收的斑点图案由随机抛物线传播模型数值模拟生成，分类任务针对15类涡旋轨道角动量（OAM）源。采用SimpleCNN和ResNet-18作为分类器，对比强度与自相关输入。量化了训练集规模与接收窗口错位的影响。由于额外传播样本获取成本高，提出一种类别条件扩散模型用于生成增强。主要贡献为频谱感知扩散目标：像素域损失结合傅里叶域Bregman正则项，以保留高频斑点统计特性。证明该混合目标与扩散模型后验均值回归目标一致，并表明生成样本显著提升低数据场景下的分类性能。
原文摘要 · Abstract (English)
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.
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