  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 coarse-to-fine generator architecture can increase the effective
resolution by a factor of eight in each spatial direction, or an overall
increase in number of pixels by a factor of 64. We also show that even just a
few examples of human-generated image segmentations can greatly improve --
qualitatively and quantitatively -- the generated images. We demonstrate our
method on works such as Leonardo's Madonna of the carnation and the
underdrawing in his Virgin of the rocks, which pose several special problems in
style transfer, including the paucity of representative works from which to
learn and transfer style information.
