TY  - JOUR
TI  - Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece
EP  - 8
Y1  - 2019/08/30/
AV  - public
VL  - 5
N1  - Distributed under a Creative Commons Attribution License 4.0 (CC BY).
This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/
IS  - 8
ID  - discovery10086806
N2  - X-ray images of polyptych wings, or other artworks painted on both sides of their support, contain in one image content from both paintings, making them difficult for experts to ?read.? To improve the utility of these x-ray images in studying these artworks, it is desirable to separate the content into two images, each pertaining to only one side. This is a difficult task for which previous approaches have been only partially successful. Deep neural network algorithms have recently achieved remarkable progress in a wide range of image analysis and other challenging tasks. We, therefore, propose a new self-supervised approach to this x-ray separation, leveraging an available convolutional neural network architecture; results obtained for details from the Adam and Eve panels of the Ghent Altarpiece spectacularly improve on previous attempts.
PB  - AMER ASSOC ADVANCEMENT SCIENCE
UR  - https://doi.org/10.1126/sciadv.aaw7416
JF  - Science Advances
A1  - Sabetsarvestani, Z
A1  - Sober, B
A1  - Higgitt, C
A1  - Daubechies, I
A1  - Rodrigues, MRD
ER  -