Synthetically generated clouds on ground-based solar observations

A dataset consisting of Ca II &amp; H-alpha images taken at the Paris Meudon Observatory. Synthetically generated cloud coverage has been applied to clean images, thereby creating an (cloudy, clean) pair--facilitating the training of cloud-removal algorithms. <strong>Data description</strong> The Ca-II and H-α synthetic dataset comprise respectively 319 and 367 pairs of shadow/shadow-free images, split into 223/96 and 256/111 training/testing pairs. Listed here are two zip archives: filament-bounding-boxes.zip -- bounding boxes of filaments that were used to compute the patched metrics. synthetic-clouds.zip -- the cloudy input/clean output images that are used to train machine learning algorithms. A PyTorch dataset has been created that handles the download, importing, and usage of this dataset. You can find this code at the github repository: https://github.com/jaypmorgan/cloud-removal <strong>Pre-processing routines</strong> To generate this set of data, we have applied a series of pre-processing routines. These are: Correct determination of the solar limb (source code can be found at: https://gitlab.lis-lab.fr/presage/solar-limb-detection). Scaling the solar disk to 420 pixels, and centring it at 511.5 pixels in the x and y dimensions. Setting background values outside the solar disk to 0. Normalising the disk intensity values into the range of 0-1.