  Flow image super-resolution (FISR) aims at recovering high-resolution
turbulent velocity fields from low-resolution flow images. Existing FISR
methods mainly process the flow images in natural image patterns, while the
critical and distinct flow visual properties are rarely considered. This
negligence would cause the significant domain gap between flow and natural
images to severely hamper the accurate perception of flow turbulence, thereby
undermining super-resolution performance. To tackle this dilemma, we
comprehensively consider the flow visual properties, including the unique flow
imaging principle and morphological information, and propose the first flow
visual property-informed FISR algorithm. Particularly, different from natural
images that are constructed by independent RGB channels in the light field,
flow images build on the orthogonal UVW velocities in the flow field. To
empower the FISR network with an awareness of the flow imaging principle, we
propose quaternion spatial modeling to model this orthogonal spatial
relationship for improved FISR. Moreover, due to viscosity and surface tension
characteristics, fluids often exhibit a droplet-like morphology in flow images.
Inspired by this morphological property, we design the dynamic flow convolution
to effectively mine the morphological information to enhance FISR. Extensive
experiments on the newly acquired flow image datasets demonstrate the
state-of-the-art performance of our method. Code and data will be made
available.
