We apply generative adversarial convolutional neural networks to the problem of style transfer to underdrawings and ghost-images in x-rays of fine art paintings with a special focus on enhancing their spatial resolution. We build upon a neural architecture developed for the related
problem of synthesizing high-resolution photo-realistic image from semantic label maps. Our neural architecture achieves high resolution through a hierarchy of generators and discriminator sub-networks, working throughout a range of spatial resolutions. This
George H. Cann, Anthony Bourached, Ryan-Rhys Griffths, David G. Stork, "Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Computer Vision and Image Analysis of Art, 2021, pp 17-1 - 17-8, https://doi.org/10.2352/ISSN.2470-1173.2021.14.CVAA-017