High-resolution (HR) information of fluid flows, although preferable, is usually less
accessible due to limited computational or experimental resources. In many cases, fluid data
are generally sparse, incomplete, and possibly noisy. How to enhance spatial resolution and
decrease the noise level of flow data is essential and practically useful. Deep learning (DL)
techniques have been demonstrated to be effective for super-resolution (SR) tasks, which,
however, primarily rely on sufficient HR labels for training. In this work, we present a novel …

High-resolution (HR) information of fluid flows, although preferable, is usually less
accessible due to limited computational or experimental resources. In many cases, fluid data
are usually sparse, incomplete, and possibly noisy. How to enhance spatial resolution and
decrease noise levels of fluid flow data is important and practically useful. Deep learning
(DL) techniques have been demonstrated effective for super-resolution (SR) tasks, which,
however, largely relies on sufficient HR labeled data for training. In this work, we present a …