A modified optical flow algorithm based on bilateral-filter and multi-resolution analysis for PIV image processing

Particle Image Velocimetry (PIV) technology is an efficient and powerful testing method to investigate the characteristics of flow field. The topic of PIV post-processing techniques has roused researchers' wide concern for its great influence on the success of flow field measurement. The traditional correlation algorithms have their innate defects. In the present study, a modified optical flow algorithm is proposed to overcome these deficiencies based on bilateral-filter and multi-resolution analysis of PIV image processing. The algorithm is designed based on the principle of multilayer segments, in which the isotropic diffusion method is employed to calculate the low-resolution layer of the image and the nonlinear filtering method is used to process the high-resolution layer. This new algorithm can reduce image noise effectively and maintain the details of the image boundary. In addition, the design of nonlinear filter makes the optical flow equation simpler, and the optimal velocity mapping factor method needs less iteration and reduces the computational load. The algorithm is first tested on synthetic time-resolved channel flow images, and the computational results from the simulated particle images are found to be in reasonable agreement with the given simulated data. The algorithm is then applied to images of actual up-channel flow, and the results also confirmed that the algorithm proposed in the present study has good performance and reliability for post-processing PIV images.