We present a method for cloud-removal from satellite images using axial transformer networks. The method considers a set of multitemporal images in a given region of interest together with the corresponding cloud masks, and delivers a cloud-free image for a specific day of the year. We propose the combination of an encoder-decoder model employing axial attention layers for the estimation of the low-resolution cloud-free image, together with a fully parallel upsampler that reconstructs the image at full resolution. The method is compared with various baselines and state-of-the-art methods on two Sentinel-2 datasets, showing significant improvements across multiple standard metrics used for image quality assessment.