Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses. (arXiv:1911.01490v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Jungbluth_A/0/1/0/all/0/1">Anna Jungbluth</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gitiaux_X/0/1/0/all/0/1">Xavier Gitiaux</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Maloney_S/0/1/0/all/0/1">Shane A.Maloney</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Shneider_C/0/1/0/all/0/1">Carl Shneider</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wright_P/0/1/0/all/0/1">Paul J. Wright</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kalaitzis_A/0/1/0/all/0/1">Alfredo Kalaitzis</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Deudon_M/0/1/0/all/0/1">Michel Deudon</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Baydin_A/0/1/0/all/0/1">Atılım Güneş Baydin</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gal_Y/0/1/0/all/0/1">Yarin Gal</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Munoz_Jaramillo_A/0/1/0/all/0/1">Andrés Muñoz-Jaramillo</a>
Breakthroughs in our understanding of physical phenomena have traditionally
followed improvements in instrumentation. Studies of the magnetic field of the
Sun, and its influence on the solar dynamo and space weather events, have
benefited from improvements in resolution and measurement frequency of new
instruments. However, in order to fully understand the solar cycle,
high-quality data across time-scales longer than the typical lifespan of a
solar instrument are required. At the moment, discrepancies between measurement
surveys prevent the combined use of all available data. In this work, we show
that machine learning can help bridge the gap between measurement surveys by
learning to textbf{super-resolve} low-resolution magnetic field images and
textbf{translate} between characteristics of contemporary instruments in
orbit. We also introduce the notion of physics-based metrics and losses for
super-resolution to preserve underlying physics and constrain the solution
space of possible super-resolution outputs.
Breakthroughs in our understanding of physical phenomena have traditionally
followed improvements in instrumentation. Studies of the magnetic field of the
Sun, and its influence on the solar dynamo and space weather events, have
benefited from improvements in resolution and measurement frequency of new
instruments. However, in order to fully understand the solar cycle,
high-quality data across time-scales longer than the typical lifespan of a
solar instrument are required. At the moment, discrepancies between measurement
surveys prevent the combined use of all available data. In this work, we show
that machine learning can help bridge the gap between measurement surveys by
learning to textbf{super-resolve} low-resolution magnetic field images and
textbf{translate} between characteristics of contemporary instruments in
orbit. We also introduce the notion of physics-based metrics and losses for
super-resolution to preserve underlying physics and constrain the solution
space of possible super-resolution outputs.
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