Flow Visualization for Complex Fluid Flows via a Structure-Enhanced Motion Estimator

Flow visualization through motion estimation using time-sequenced images plays a significant role in analyzing and understanding complex flow phenomena, and it is widely used in meteorology, oceanography, medicine, astronomy, experimental fluid mechanics, etc. However, it is difficult for current motion estimators to adapt to illumination changes, remove instable perturbation, and capture diverse motion patterns. In this paper, a novel flow visualization tool is developed to address these issues by employing a structure-enhanced motion estimator composed of a data term and a regularization term. Specifically, a statistical correlation descriptor is designed for the data term to improve the accuracy of motion estimation by enhancing both illumination robustness and matching discrimination. Inspired by the strong distinguishability of a structure-texture distribution in a local window, a structure-enhanced regularizer that considers the physical mechanism of fluid diffusion is introduced to capture different motion patterns, enhance prominent flow structures, and remove unnecessary ripples or textures caused by instable perturbation or noise. The experimental results demonstrate that our approach significantly outperforms current motion estimators in handling illumination changes and predicting complex fluid flows, and it also achieves state-of-the-art evaluation results on the public fluid flow datasets. Furthermore, the designed flow visualization tool successfully captures diverse motion patterns in Jupiter’s White Ovals, which is crucial for understanding the physical mechanisms behind their formation and sustenance.