STSR-INR: Spatiotemporal super-resolution for multivariate time-varying volumetric data via implicit neural representation

• Published in: Computers & graphics 2024-04, Vol.119, p.103874, Article 103874
• Main Authors: Tang, Kaiyuan, Wang, Chaoli
• Format: Article
• Language: English
• Subjects: Implicit neural representation; MATHEMATICS AND COMPUTING; Multivariate time-varying data; Spatiotemporal super-resolution
• ISSN: 0097-8493; 1873-7684
• DOI: 10.1016/j.cag.2024.01.001

Implicit neural representation (INR) has surfaced as a promising direction for solving different scientific visualization tasks due to its continuous representation and flexible input and output settings. We present STSR-INR, an INR solution for generating simultaneous spatiotemporal super-resolution for multivariate time-varying volumetric data. Inheriting the benefits of the INR-based approach, STSR-INR supports unsupervised learning and permits data upscaling with arbitrary spatial and temporal scale factors. Unlike existing GAN- or INR-based super-resolution methods, STSR-INR focuses on tackling variables or ensembles and enabling joint training across datasets of various spatiotemporal resolutions. We achieve this capability via a variable embedding scheme that learns latent vectors for different variables. In conjunction with a modulated structure in the network design, we employ a variational auto-decoder to optimize the learnable latent vectors to enable latent-space interpolation. To combat the slow training of INR, we leverage a multi-head strategy to improve training and inference speed with significant speedup. We demonstrate the effectiveness of STSR-INR with multiple scalar field datasets and compare it with conventional tricubic+linear interpolation and state-of-the-art deep-learning-based solutions (STNet and CoordNet). •INR for STSR task of multivariate time-varying volumetric data.•A multi-head strategy to speed up training and inference.•Variable embedding for joint training of diverse spatiotemporal multivariate data.•A VAD module to encode variables for latent-space interpolation.•Demonstrated advantages over state-of-the-art methods. [Display omitted]