Unsupervised particle image velocimetry (PIV) methods eliminate the need for extensive labeled datasets, yet their performance in complex flow regions, such as those with particle occlusion or out-of-boundary motion, remains limited. To address this issue, this study proposes an unsupervised PIV network: Unsupervised Enhanced LiteFlowNet3 with Cross-Correlation (UnLECNet-PIV). Built upon the LiteFlowNet3 architecture, the model incorporates structural refinements to its encoder-decoder design and integrates two core innovations: a Hierarchical Reciprocal Attention Mixer (H-RAMi) for multi-scale feature fusion and cross-correlation-derived feature priors to strengthen motion representation. Additionally, a boundary-aware loss function is introduced to refine flow estimation near image edges, complemented by a reflection padding strategy to preserve structural integrity at boundaries. Benchmark evaluations on synthetic PIV datasets demonstrate that UnLECNet-PIV reduces the average endpoint error (AEE) by approximately 25.9% compared to the baseline UnLiteFlowNet, while maintaining robustness and high spatial resolution under challenging conditions, including fine-scale vortical structures and Gaussian noise. Real experimental data further validate its physical consistency, underscoring the method’s practical effectiveness.
Ni et al. (2026) studied this question.