Applicability of the Bayesian estimation-based spatial superresolution measurement to the velocity fields of a subsonic jet acquired by simultaneous dual PIV

The spatial superresolution framework based on proper orthogonal decomposition (POD) and Bayesian estimation was designed for the velocity field reconstruction of a subsonic jet. The framework was evaluated using the experimental data obtained from simultaneous dual Particle Image Velocimetry measurements of a subsonic jet with different magnifications. The framework was first tested using the artificial low-resolution (LR) velocity field generated by average pooling the high-resolution (HR) experimental velocity field. The conventional bicubic interpolation was employed for performance comparison. The framework successfully reconstructed the high-spatial-resolution velocity field from the coarse velocity field, with the estimation error of 6% at the resolution ratio of LR:HR = 1:3, using 2000 POD modes. Its error improved with the POD mode increase, where the fine structures in high POD modes are started to be reconstructed. Framework was then applied to the experimental velocity field datasets, which its resolution ratio is 1:3. Its estimation error was 77% when using top 5 POD modes and improved to 46% when using 2000 POD modes. Since the bicubic interpolation error was 110%, the framework outperformed bicubic interpolation in all modes.