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Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties

Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun’s magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram.

Xavier Gitiaux, Shane Maloney, Anna Jungbluth, Carl Shneider, Atılım Güneş Baydin, Paul J. Wright, Yarin Gal, Michel Deudon, Freddie Kalaitzis, Andres Munoz-Jaramillo
Workshop on Bayesian Deep Learning, NeurIPS 2019

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