  We describe the application of convolutional neural network style transfer to
the problem of improved visualization of underdrawings and ghost-paintings in
fine art oil paintings. Such underdrawings and hidden paintings are typically
revealed by x-ray or infrared techniques which yield images that are grayscale,
and thus devoid of color and full style information. Past methods for inferring
color in underdrawings have been based on physical x-ray fluorescence spectral
imaging of pigments in ghost-paintings and are thus expensive, time consuming,
and require equipment not available in most conservation studios. Our
algorithmic methods do not need such expensive physical imaging devices. Our
proof-of-concept system, applied to works by Pablo Picasso and Leonardo, reveal
colors and designs that respect the natural segmentation in the ghost-painting.
We believe the computed images provide insight into the artist and associated
oeuvre not available by other means. Our results strongly suggest that future
applications based on larger corpora of paintings for training will display
color schemes and designs that even more closely resemble works of the artist.
For these reasons refinements to our methods should find wide use in art
conservation, connoisseurship, and art analysis.
