一种实现自适应相关分析的变分方法用于粒子成像测速中运动位移估算
In particle image velocimetry (PIV) a temporally separatedimage pair of a gas or liquid seeded with small particles is recordedand analysed in order to measure fluid flows therein. We investigate avariational approach to cross-correlation, a robust and well-establishedmethod to determine displacement vectors from the image data. A “soft”Gaussian window function replaces the usual rectangular correlationframe. We propose a criterion to adapt the window size and shape thatdirectly formulates the goal to minimise the displacement estimation error.In order to measure motion and adapt the window shapes at thesame time we combine both sub-problems into a bi-level optimisationproblem and solve it via continuous multiscale methods. Experimentswith synthetic and real PIV data demonstrate the ability of our approachto solve the formulated problem. Moreover window adaptationyields significantly improved results.