Reconstructing higher-resolution four-dimensional time-varying volumetric data

Abstract

We have witnessed substantial growth in super-resolution research within the computer vision community. Unlike previous works that mainly focus on the super-resolution synthesis of images, videos, or single volumes, our research is dedicated to the super-resolution synthesis of time-varying volumetric data, which are generated from scientific simulations and are crucial for domain scientists to understand and analyse complex scientific phenomena. Compared to previous works, our research presents a greater challenge: the time-varying volumetric data have higher dimensions, making it more difficult to synthesise super-resolution that maintains good spatio-temporal consistency while achieving high visual quality. To tackle this challenge, we introduce a new GAN-based network called SSR-DoubleUNetGAN, which includes novel network architecture and loss functions, allowing for accurate synthesis of spatial super-resolution for time-varying volumetric data with relatively fast training time. Our method can be applied in the context of in-situ visualisation to aid domain scientists in analysing more time-varying volumetric data more efficiently. In addition, it can be used in the compression-decompression pipeline to recover the super-resolution time-varying volumetric data from their low-resolution counterpart. To demonstrate its effectiveness, we applied various time-varying volumetric datasets from different scientific simulations to it. To demonstrate its advantages, we compared it qualitatively and quantitatively with five state-of-the-art super-resolution techniques, namely SSR-TVD, Tricubic, SRResNet, Cubic, and Linear. Furthermore, we conducted an ablation study to validate its important modules. The experimental results show that our method outperforms the compared state-of-the-art techniques.