Kavli Affiliate: Kyle Shen | First 5 Authors: Jingyi Shen, Jingyi Shen, , , | Summary: 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 […]
Continue.. PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data