Surrogate-based cross-correlation for particle image velocimetry

Abstract

This paper presents a novel surrogate-based cross-correlation (SBCC) framework to improve the correlation performance for practical particle image velocimetry (PIV). The basic idea is that an optimized surrogate filter/image, replacing one raw image, will produce a more accurate and robust correlation signal. Specifically, the surrogate image is encouraged to generate a perfect Gaussian-shaped correlation map to tracking particles (PIV image pair) while producing zero responses to image noise (context images). The problem is formulated with an objective function composed of surrogate loss and consistency loss. As a result, the closed-form solution provides a multivariate operator that could consider other negative context images. Compared with state-of-the-art baseline methods (background subtraction, robust phase correlation, etc.), our SBCC method exhibits significant performance improvement (accuracy and robustness) in the synthetic dataset and several challenging experimental PIV cases. Moreover, our implementation with all details (https://github.com/yongleex/SBCC) is also available for interested researchers.