The integration of deep learning with optical flow estimation in Particle Image Velocimetry (PIV) represents an emerging solution. Extensive research indicates that deep learning has potential to match or outperform state-of-the-art classical algorithms in efficiency, accuracy, and spatial resolution. However, current learning-based methods, which rely on cost volumes and convolutions for flow regression, are limited to local correlations. This limitation hinders the capture of global information. While extensive iterative refinements enhance the quality of prediction flows, they also result in a linear increase in inference time. To enhance both efficiency and accuracy, we propose a global matching method for PIV. This method directly compares feature similarities to identify correspondences between images and generate estimated flows. The underlying idea is to first extract initial features of particle image pairs, then enhance these features through a Transformer specifically designed for PIV, and perform operations for feature correlation matching, followed by global optical flow propagation and optimization. Additionally, higher-resolution features are introduced for refinement. By employing both synthetic and experimental data, including benchmark sets and data from turbulent wave channel flow experiments, we demonstrate that global matching method in PIV achieves superior efficiency and accuracy compared to existing learning-based methods.

Conventional particle image velocimetry (PIV) techniques face an inherent trade-off between temporal resolution and single-pulse laser energy, limiting their performance in shock wave measurements. This study develops a multi-channel parallel PIV system with programmable timing control to address this constraint. By distributing laser pulses across eight independent optical channels, the system decouples single-pulse energy from the repetition rate, enabling continuous velocity field measurements at microsecond temporal resolution with high signal-to-noise ratio. Experiments were conducted on a planar shock wave propagating over a trapezoidal step in a shock tube facility. The system captured velocity fields at seven consecutive time instants with a 1 μs pulse interval, revealing the curved evolution of diffracted shock fronts and transient flow separation induced by shock–boundary-layer interaction. Comparison with unsteady Reynolds-averaged Navier–Stokes simulations demonstrated that the PIV system resolves fine-scale post-shock perturbations and turbulent fluctuations that were numerically dissipated in computational fluid dynamics. The results verify that the parallel-channel architecture accurately captures unsteady flow structures during shock reflection and diffraction, offering a reliable diagnostic technique for investigations of shock wave dynamics.