Vision-informed flow field super-resolution with quaternion spatial modeling and dynamic fluid convolution

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