STSRNet: Deep Joint Space–Time Super-Resolution for Vector Field Visualization.

We propose STSRNet, a joint space–time super-resolution deep learning based model for time-varying vector field data. Our method is designed to reconstruct high temporal resolution and high spatial resolution vector fields sequence from the corresponding low-resolution key frames. For large scale simulations, only data from a subset of time steps with reduced spatial resolution can be stored for post hoc analysis. In this article, we leverage a deep learning model to capture the nonlinear complex changes of vector field data with a two-stage architecture: the first stage deforms a pair of low spatial resolution (LSR) key frames forward and backward to generate the intermediate LSR frames, and the second stage performs spatial super-resolution to output the high-resolution sequence. Our method is scalable and can handle different datasets. We demonstrate the effectiveness of our framework with several datasets through quantitative and qualitative evaluations. [ABSTRACT FROM AUTHOR]