# Solar Image Restoration with the Cycle-GAN Based on Multi-Fractal Properties of Texture Features

## TL;DR

This paper introduces a Cycle-GAN based method leveraging multi-fractal texture features to restore solar images, enhancing resolution without requiring paired datasets or additional hardware.

## Contribution

The paper presents a novel, purely data-driven solar image restoration technique using Cycle-GANs and multi-fractal texture assumptions, applicable without paired training data.

## Key findings

- Improves spatial resolution of solar images
- Works with simulated and real data
- Does not require paired datasets or extra instruments

## Abstract

Texture is one of the most obvious characteristics in solar images and it is normally described by texture features. Because textures from solar images of the same wavelength are similar, we assume texture features of solar images are multi-fractals. Based on this assumption, we propose a pure data-based image restoration method: with several high resolution solar images as references, we use the Cycle-Consistent Adversarial Network to restore burred images of the same steady physical process, in the same wavelength obtained by the same telescope. We test our method with simulated and real observation data and find that our method can improve the spatial resolution of solar images, without loss of any frames. Because our method does not need paired training set or additional instruments, it can be used as a post-processing method for solar images obtained by either seeing limited telescopes or telescopes with ground layer adaptive optic system.