Bourached, Anthony;
(2023)
Style transfer for improved visualization of underdrawings and ghost paintings: An application to a work by Vincent van Gogh.
In:
IS&T International Symposium on Electronic Imaging 2023 / Computer Vision and Image Analysis of Art 2023.
(pp. 209-1-209-5).
Society for Imaging Science and Technology
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Abstract
We applied computational style transfer, specifically coloration and brush stroke style, to achromatic images of a ghost painting beneath Vincent van Gogh’s Still life with meadow flowers and roses. Our method is an extension of our previous work in that it used representative artworks by the ghost painting’s author to train a Generalized Adversarial Network (GAN) for integrating styles learned from stylistically distinct groups of works. An effective amalgam of these learned styles is then transferred to the target achromatic work.
| Type: | Proceedings paper |
|---|---|
| Title: | Style transfer for improved visualization of underdrawings and ghost paintings: An application to a work by Vincent van Gogh |
| Event: | Computer Visiona and Analysis of Art |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.2352/EI.2023.35.13.CVAA-209 |
| Publisher version: | https://doi.org/10.2352/EI.2023.35.13.CVAA-209 |
| 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-painting, style transfer, deep neural network, computational art analysis, artificial intelligence, Generalized Adversarial Network (GAN), 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/10209695 |
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