Pu, Wei;
Huang, Junjie;
Sober, Barak;
Daly, Nathan;
Higgitt, Catherine;
Dragotti, Pier Luigi;
Daubechies, Ingrid;
(2021)
A Learning Based Approach to Separate Mixed X-Ray Images Associated with Artwork with Concealed Designs.
In:
2021 29th European Signal Processing Conference (EUSIPCO).
(pp. pp. 1491-1495).
IEEE: Dublin, Ireland.
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Abstract
X-ray images are widely used in the study of paintings. When a painting has hidden sub-surface features (e.g., reuse of the canvas or revision of a composition by the artist), the resulting X-ray images can be hard to interpret as they include contributions from both the surface painting and the hidden design. In this paper we propose a self-supervised deep learning-based image separation approach that can be applied to the X-ray images from such paintings (‘mixed X-ray images’) to separate them into two hypothetical X-ray images, one containing information related to the visible painting only and the other containing the hidden features. The proposed approach involves two steps: (1) separation of the mixed X-ray image into two images, guided by the combined use of a reconstruction and an exclusion loss; (2) even allocation of the error map into the two individual, separated X-ray images, yielding separation results that have an appearance that is more familiar in relation to Xray images. The proposed method was demonstrated on a real painting with hidden content, Doña Isabel de Porcel by Francisco de Goya, to show its effectiveness.
| Type: | Proceedings paper |
|---|---|
| Title: | A Learning Based Approach to Separate Mixed X-Ray Images Associated with Artwork with Concealed Designs |
| Event: | 29th European Signal Processing Conference (EUSIPCO) |
| Location: | ELECTR NETWORK |
| Dates: | 23 Aug 2021 - 27 Aug 2021 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.23919/EUSIPCO54536.2021.9616096 |
| Publisher version: | http://dx.doi.org/10.23919/EUSIPCO54536.2021.96160... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Acoustics, Computer Science, Software Engineering, Engineering, Electrical & Electronic, Imaging Science & Photographic Technology, Telecommunications, Computer Science, Engineering, Art Investigation, Image Separation, Deep Neural Networks, Convolutional Neural Networks |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10151201 |
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