  The study and prediction of space weather entails the analysis of solar
images showing structures of the Sun's atmosphere. When imaged from the Earth's
ground, images may be polluted by terrestrial clouds which hinder the detection
of solar structures. We propose a new method to remove cloud shadows, based on
a U-Net architecture, and compare classical supervision with conditional GAN.
We evaluate our method on two different imaging modalities, using both real
images and a new dataset of synthetic clouds. Quantitative assessments are
obtained through image quality indices (RMSE, PSNR, SSIM, and FID). We
demonstrate improved results with regards to the traditional cloud removal
technique and a sparse coding baseline, on different cloud types and textures.
