# Enhancing SDO/HMI images using deep learning

## TL;DR

This paper introduces Enhance, a deep learning method that enhances HMI solar images by deconvolving and super-resolving them, achieving higher spatial resolution similar to a larger telescope, thus enabling better analysis of small-scale solar phenomena.

## Contribution

The paper presents a novel deep learning approach that combines deconvolution and super-resolution to improve HMI solar images, trained on synthetic simulation data.

## Key findings

- Enhanced images mimic larger telescope observations
- Good consistency with Hinode data after degradation
- Open source code available for community use

## 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.