Title: Enhancing SDO/HMI images using deep learning,
Abstract: The Helioseismic and Magnetic Imager (HMI) provides continuum images and
magnetograms with a cadence better than one per minute. It has been
continuously observing the Sun 24 hours a day for the past 7 years. The obvious
trade-off between full disk observations and spatial resolution makes HMI not
enough to analyze the smallest-scale events in the solar atmosphere. Our aim is
to develop a new method to enhance HMI data, simultaneously deconvolving and
super-resolving images and magnetograms. The resulting images will mimic
observations with a diffraction-limited telescope twice the diameter of HMI.
Our method, which we call Enhance, is based on two deep fully convolutional
neural networks that input patches of HMI observations and output deconvolved
and super-resolved data. The neural networks are trained on synthetic data
obtained from simulations of the emergence of solar active regions. We have
obtained deconvolved and supper-resolved HMI images. To solve this ill-defined
problem with infinite solutions we have used a neural network approach to add
prior information from the simulations. We test Enhance against Hinode data
that has been degraded to a 28 cm diameter telescope showing very good
consistency. The code is open source.