# Enhancing SDO/HMI images using deep learning

arXiv: https://arxiv.org/abs/1706.02933 · HTML: https://mlsanity.com/paper/1706.02933/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2017.

## Built on by (most cited first, 11 of 11)

- [Machine learning in solar physics](https://mlsanity.com/paper/2306.15308/) (2023)
- [Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation](https://mlsanity.com/paper/2208.09512/) (2022)
- [Surya: Foundation Model for Heliophysics](https://mlsanity.com/paper/2508.14112/) (2025)
- [AI Foundation Model for Heliophysics: Applications, Design, and Implementation](https://mlsanity.com/paper/2410.10841/) (2024)
- [Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network](https://mlsanity.com/paper/2403.18302/) (2024)
- [Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges](https://mlsanity.com/paper/2409.04850/) (2024)
- [Solar Active Regions Detection Via 2D Circular Kernel Time Series Transformation, Entropy and Machine Learning Approach](https://mlsanity.com/paper/2306.08270/) (2023)
- [Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model](https://mlsanity.com/paper/2503.03959/) (2025)
- [Contrastive Heliophysical Image Pretraining for Solar Dynamics Observatory Records](https://mlsanity.com/paper/2511.22958/) (2025)
- [Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation](https://mlsanity.com/paper/2609.27131/) (2026)
- [Cross-Modal Solar Image Synthesis: Adapting the Surya Foundation Model from He I 10830 AA to EUV Translation and Coronal Hole Segmentation](https://mlsanity.com/paper/2610.04553/) (2026)

## Abstract (arXiv)

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.