Abstract
Deep learning algorithms have significantly reduced the computational time and improved the spatial resolution of particle image velocimetry~(PIV). However, the models trained on synthetic datasets might have a degraded performance on practical particle images due to domain gaps. As a result, special residual patterns are often observed for the vector fields of deep learning-based estimators. To reduce the special noise step-by-step, we employ a denoising diffusion model~(FlowDiffuser) for PIV analysis. And the data-hungry iterative denoising diffusion model is trained via a transfer learning strategy, resulting in our PIV-FlowDiffuser method. Specifically, (1) pre-training a FlowDiffuser model with multiple optical flow datasets of the computer vision community, such as Sintel, KITTI, etc; (2) fine-tuning the pre-trained model on synthetic PIV datasets. Note that the PIV images are upsampled by a factor of two to resolve the small-scale turbulent flow structures. The visualized results indicate that our PIV-FlowDiffuser effectively suppresses the noise patterns. Therefore, the denoising diffusion model reduces the average end-point error~($AEE$) by 59.4% over RAFT256-PIV baseline on the classic Cai's dataset. Besides, PIV-FlowDiffuser exhibits enhanced generalization performance on unseen particle images due to transfer learning. Overall, this study highlights the transfer-learning-based denoising diffusion models for PIV. And a detailed implementation is recommended for interested readers in the repository this https URL.
Abstract (translated)
深度学习算法已显著减少了粒子图像 velocimetry (PIV) 的计算时间，并提高了其空间分辨率。然而，基于合成数据集训练的模型在实际颗粒图像上的性能可能会因领域差距而下降。因此，在基于深度学习的估计器产生的向量场中经常会观察到特殊的噪声模式。为了逐步减少这种特殊噪声，我们使用了一种去噪扩散模型（FlowDiffuser）来进行 PIV 分析。通过采用迁移学习策略训练数据驱动型迭代去噪扩散模型，最终形成了我们的PIV-FlowDiffuser方法。 具体来说： 1. 使用计算机视觉社区中的多个光学流数据集（如Sintel、KITTI等）对 FlowDiffuser 模型进行预训练。 2. 在合成的 PIV 数据集上微调经过预训练的模型。需要注意的是，通过将PIV图像放大两倍来解析小规模湍流结构。 可视化结果表明，我们的PIV-FlowDiffuser方法能够有效抑制噪声模式。因此，在经典的Cai数据集上，与RAFT256-PIV基线相比，去噪扩散模型使平均终点误差（$AEE$）降低了59.4%。此外，由于采用了迁移学习，PIV-FlowDiffuser 在未见过的颗粒图像上的泛化性能得到了增强。 总的来说，这项研究强调了基于迁移学习的去噪扩散模型在 PIV 中的应用价值，并推荐有兴趣的读者参考该研究的详细实现（详见此链接：this https URL）。
URL
https://arxiv.org/abs/2504.14952
