用神经网络直接估计流体图像位移，无需训练即可高分辨率测速。
Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids
- 基于光流方程与神经网络，直接预测图像间连续位移场。
- 在合成与实验图像上均实现高精度瞬时速度与湍流统计量估计。
- 无需预训练，可即插即用，适合实时流体分析场景。
实验流体力学中的重要工具是粒子图像测速（PIV）。尽管已有多种稳健方法可用于从图像中估算速度场，但提升结果空间分辨率仍需新方法。本文提出一种基于神经网络与光流方程的新方法，通过连续预测序列图像间的位移向量，实现图像全空间分辨率的位移场表示。该方法在合成与实验图像上进行了验证，结果显示其在瞬时速度场、平均湍流量及功率谱密度估计方面均具有高精度。与以往机器学习方法不同，该方法无需任何预训练，可直接应用于任意一对图像。
原文摘要 · Abstract (English)
An important tool for experimental fluids mechanics research is Particle Image Velocimetry (PIV). Several robust methodologies have been proposed to perform the estimation of velocity field from the images, however, alternative methods are still needed to increase the spatial resolution of the results. This work presents a novel approach for estimating fluid flow fields using neural networks and the optical flow equation to predict displacement vectors between sequential images. The result is a continuous representation of the displacement, that can be evaluated on the full spatial resolution of the image. The methodology was validated on synthetic and experimental images. Accurate results were obtained in terms of the estimation of instantaneous velocity fields, and of the determined time average turbulence quantities and power spectral density. The methodology proposed differs of previous attempts of using machine learning for this task: it does not require any previous training, and could be directly used in any pair of images.
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