Fully convolutional neural networks for processing observational data from small remote solar telescopes.

Heliophysics phenomena on the Sun, such as radio bursts, can strongly affect satellites and ground-based electronic systems. Therefore, an insight into the actual image of the Sun with good spatial and temporal resolution is crucial. In this paper, we explore the possibility of using fully convolutional networks (FCNs) to improve the images acquired from remotely operated small solar telescopes whose resolution is limited by the size of the lens aperture and by atmospheric turbulence. For this purpose, we use chromosphere data from the 50 mm small Hα Telescope of the Silesian University of Technology acquired over many months under various atmospheric conditions. We compare the obtained results with the results of raw data processing by a state-of-the-art deterministic algorithm, multi-frame blind deconvolution (MFBD). In our research, we investigate the impact of the amount of data and the complexity of FCNs on the quality of the results and their processing time. We show that the use of FCNs is a very attractive alternative to MFBD because they are more energy efficient and allow for the obtaining of comparable results in orders of magnitude shorter time.