An effective superkernel-based optical flow network for particle image velocimetry

Particle image velocimetry (PIV) is a key non-intrusive technique for velocity measurement, yet dense flow estimation remains difficult in cases with small-scale vortices or occlusions. Based on Super-Kernel Flow Network (SKFlow), a novel lightweight deep learning framework called PIV-LightSKFlow has been proposed, integrating a feature encoder, multi-scale correlation pyramid, superkernel module, and global motion aggregation (GMA). Paired PIV images are processed to predict dense velocity fields. For PIV tasks, the encoder resolution is improved from 1/8 to 1/4 for better particle representation, and the correlation pyramid is redesigned, reducing parameters by 29.3%. A new synthetic dataset is created to evaluate the algorithm's ability to predict small occlusions in the flow field. Evaluations on synthetic and experimental datasets show that PIV-LightSKFlow surpasses traditional algorithms, and previous deep learning methods. It achieves root mean square error reductions of 6.9%-68.4% in velocity prediction of flow fields such as vortex shedding, and direct numerical simulation turbulent motion, while maintaining high spatial resolution and stable performance at various Reynolds numbers (Re). The superkernel and GMA modules further enhance stability and accuracy in occluded regions. These results highlight PIV-LightSKFlow as an efficient and reliable solution for advanced PIV estimation, with promising potential in experimental and industrial fluid mechanics.