# Surrogate-based cross-correlation for particle image velocimetry

arXiv: https://arxiv.org/abs/2112.05303 · HTML: https://mlsanity.com/paper/2112.05303/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 86 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018)
- [Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning](https://mlsanity.com/paper/2107.02339/) (2021)
- [Unsupervised Learning of Particle Image Velocimetry](https://mlsanity.com/paper/2007.14487/) (2020)
- [Neural Particle Image Velocimetry](https://mlsanity.com/paper/2101.11950/) (2021)
- [A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded](https://mlsanity.com/paper/2111.00395/) (2021)
- [Blind Inverse Gamma Correction with Maximized Differential Entropy](https://mlsanity.com/paper/2007.02246/) (2020)
- [Diffeomorphic Particle Image Velocimetry](https://mlsanity.com/paper/2108.07438/) (2021)

## Built on by (most cited first, 2 of 2)

- [SynthPix: A lightspeed PIV image generator](https://mlsanity.com/paper/2512.09664/) (2025)
- [PIV-FlowDiffuser:Transfer-learning-based denoising diffusion models for PIV](https://mlsanity.com/paper/2504.14952/) (2025)

## Abstract (arXiv)

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 (urlhttps://github.com/yongleex/SBCC) is also available for interested researchers.