  The sparse layouts of radio interferometers result in an incomplete sampling
of the sky in Fourier space which leads to artifacts in the reconstructed
images. Cleaning these systematic effects is essential for the scientific use
of radiointerferometric images. Established reconstruction methods are often
time-consuming, require expert-knowledge, and suffer from a lack of
reproducibility. We have developed a prototype Deep Learning-based method that
generates reproducible images in an expedient fashion. To this end, we take
advantage of the efficiency of Convolutional Neural Networks to reconstruct
image data from incomplete information in Fourier space. The Neural Network
architecture is inspired by super-resolution models that utilize residual
blocks. Using simulated data of radio galaxies that are composed of Gaussian
components we train Deep Learning models whose reconstruction capability is
quantified using various measures. The reconstruction performance is evaluated
on clean and noisy input data by comparing the resulting predictions with the
true source images. We find that source angles and sizes are well reproduced,
while the recovered fluxes show substantial scatter, albeit not worse than
existing methods without fine-tuning. Finally, we propose more advanced
approaches using Deep Learning that include uncertainty estimates and a concept
to analyze larger images.
