  The contrasts and magnitude of observable signatures of small-scale features
degrade as angular resolution decreases. High-cadence time-series of synthetic
observable maps at 1.25 mm were produced from 3D magnetohydrodynamic Bifrost
simulations of the solar atmosphere and degraded to the angular resolution
corresponding to observational data with the Atacama Large
Millimeter/sub-millimeter Array (ALMA). The Deep Solar ALMA Neural Network
Estimator (Deep-SANNE) is an artificial neural network trained to improve the
resolution and contrast of solar observations. This is done by recognizing
dynamic patterns in both the spatial and temporal domains of small-scale
features at an angular resolution corresponding to observational data and
correlated them to highly resolved nondegraded data from the
magnetohydrodynamic simulations. A second simulation, was used to validate the
performance. Deep-SANNE provides maps of the estimated degradation of the
brightness temperature, which can be used to filter for locations that most
probably show a high accuracy and as correction factors in order to construct
refined images that show higher contrast and more accurate brightness
temperatures than at the observational resolution. Deep-SANNE reveals more
small-scale features and estimates the excess temperature of brightening events
with an average accuracy of 94.0% relative to the highly resolved data,
compared to 43.7% at the observational resolution. By using the additional
information of the temporal domain, Deep-SANNE can restore high contrasts
better than a standard two-dimensional deconvolver technique. Deep-SANNE is
applied on observational solar ALMA data. The Deep-SANNE refined images are
useful for analysing small-scale and dynamic features. They can identify
locations in the data with high accuracy for an in-depth analysis and allow a
more meaningful interpretation of solar observations.
