Surrogate-based cross-correlation for particle image velocimetry

TL;DR

This paper introduces a surrogate-based cross-correlation framework that enhances particle image velocimetry accuracy and robustness by optimizing surrogate filters to produce ideal correlation maps, outperforming existing methods.

Contribution

The novel SBCC framework formulates an objective to generate surrogate images that improve correlation accuracy, providing an efficient solution with demonstrated performance gains over state-of-the-art techniques.

Findings

Significant accuracy improvements over baseline methods.

Enhanced robustness in challenging PIV cases.

Effective surrogate filter optimization demonstrated on datasets.

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 perfect Gaussian-shaped correlation map to tracking particles (PIV image pair) while producing zero responses to image noise (context images). And the problem is formularized with an objective function composed of surrogate loss and consistency loss. As a result, the closed-form solution provides an efficient multivariate operator that could consider other negative context images. Compared with the state-of-the-art baseline methods (background subtraction, robust phase correlation, etc.), our SBCC method exhibits significant performance improvement (accuracy and robustness) on the synthetic dataset and several challenging experimental PIV cases. Besides, our implementation with experimental details (\url{https://github.com/yongleex/SBCC}) is also available for interested researchers.