Year: 2022
Type: article
Abstract: We present a novel technique for hierarchical super resolution (SR) with neural networks (NNs), which upscales volumetric data represented with an octree data structure to a high-resolution uniform gridwith minimal seam artifacts on octree node boundaries. Our method uses existing state-of-the-art SR models and adds flexibility to upscale input data with varying levels of detail across the domain, instead of only uniform grid data that are supported in previous approaches.The key is to use a hierarchy of SR NNs, each trained to perform 2× SR between two levels of detail, with a hierarchical SR algorithm that minimizes seam artifacts by starting from the coarsest level of detail and working up.We show that our hierarchical approach outperforms baseline interpolation and hierarchical upscaling methods, and demonstrate the usefulness of our proposed approach across three use cases including data reduction using hierarchical downsampling+SR instead of uniform downsampling+SR, computation savings for hierarchical finite-time Lyapunov exponent field calculation, and super-resolving low-resolution simulation results for a high-resolution approximation visualization.
Institutions: The Ohio State University, Argonne National Laboratory
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G102163711, G139908954, AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE), G2287611682, G262962599, III: Medium: Collaborative Research: Deep Learning for In Situ Analysis and Visualization, G498139845, G513797252, G5398989114, RAPIDS2: A SCIDAC INSTITUTE FOR COMPUTER SCIENCE, DATA, AND ARTIFICIAL INTELLIGENCE, G6848031779, G6918803902, G8202656448, G878601127, G8963150325, G969889393, G993149817
OpenAlex ID: W4306701786