A Higher-Order Model for Fluid Motion Estimation

Image-based fluid motion estimation is of interest to science and engineering. Flow-estimation methods often rely on physics-based or spline-based parametric models, as well as on smoothing regularizers. The calculation of physics models can be involved, and commonly used 2nd-order regularizers can be biased towards lower-order flow fields. In this paper, we propose a local parametric model based on a linear combination of complex-domain basis flows, and a resulting global field that is produced by blending together local models using partition-of-unity. We show that the global field can be regularized to an arbitrary order without bias towards specific flows. Additionally, the blending approach to fluid-motion estimation is more flexible than competing spline-based methods. We obtained promising results on both synthetic and real fluid data.