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Breakthroughs in our understanding of physical phenomena have traditionallyfollowed improvements in instrumentation. Studies of the magnetic field of theSun, and its influence on the solar dynamo and space weather events, havebenefited from improvements in resolution and measurement frequency of newinstruments. However, in order to fully understand the solar cycle,high-quality data across time-scales longer than the typical lifespan of asolar instrument are required. At the moment, discrepancies between measurementsurveys prevent the combined use of all available data. In this work, we showthat machine learning can help bridge the gap between measurement surveys bylearning to \textbf{super-resolve} low-resolution magnetic field images and\textbf{translate} between characteristics of contemporary instruments inorbit. We also introduce the notion of physics-based metrics and losses forsuper-resolution to preserve underlying physics and constrain the solutionspace of possible super-resolution outputs.

Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses