Enhancing experimental image quality in two-phase bubbly systems with super-resolution using generative adversarial networks

Fluid Dynamics is a key scientific field to multitudes of engineering applications. Experimental work in this field requires careful set-up and expensive image-capturing equipment, particularly when considering the finer details of complex phenomena. In this work, we study the application of super-resolution Generative Adversarial Networks (GANs) to achieve high-resolution results by upscaling lower-resolution experimental images. We train GANs proposed for natural images on a bubbly flow experimental Fluid Dynamics dataset and compare common super-resolution evaluation metrics to domain expert assessments of the upscaled images. We find that these models achieve promising results, as evaluated by experts, and transfer learning from natural images translates to better performance overall. Attention mechanisms are found to be particularly useful in recreating sharper details. On the other hand, traditional super-resolution evaluation metrics are found to align poorly with expert perception of quality, signaling the need for better systematic evaluation methodologies in this domain.