  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.
