PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data. (arXiv:2308.04605v1 [eess.IV])

Although many deep-learning-based super-resolution approaches have been
proposed in recent years, because no ground truth is available in the inference
stage, few can quantify the errors and uncertainties of the super-resolved
results. For scientific visualization applications, however, conveying
uncertainties of the results to scientists is crucial to avoid generating
misleading or incorrect information. In this paper, we propose PSRFlow, a novel
normalizing flow-based generative model for scientific data super-resolution
that incorporates uncertainty quantification into the super-resolution process.
PSRFlow learns the …

applications arxiv data errors flow inference scientists stage super resolution visualization