Bourached, Anthony;
Cann, George H;
Griffths, Ryan-Rhys;
Stork, David G;
(2021)
Recovery of underdrawings and ghost-paintings via
style transfer by deep convolutional neural networks:
A digital tool for art scholars.
Electronic Imaging
, 33
, Article art00006. 10.2352/issn.2470-1173.2021.14.cvaa-042.
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Abstract
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 uorescence 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-ofconcept 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.
| Type: | Article |
|---|---|
| Title: | Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.2352/issn.2470-1173.2021.14.cvaa-042 |
| Publisher version: | https://doi.org/10.2352/issn.2470-1173.2021.14.cva... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Ghost-paintings, style transfer, deep neural network, computational art analysis, artificial intelligence, computer-assisted connoisseurship |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10209687 |
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