Pu, Wei;
Huang, Jun-Jie;
Sober, Barak;
Daly, Nathan;
Higgitt, Catherine;
Daubechies, Ingrid;
Dragotti, Pier Luigi;
(2022)
Mixed X-Ray Image Separation for Artworks with Concealed Designs.
IEEE Transactions on Image Processing
(In press).
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Abstract
In this paper, we focus on X-ray images (Xradiographs) of paintings with concealed sub-surface designs (e.g., deriving from reuse of the painting support or revision of a composition by the artist), which therefore include contributions from both the surface painting and the concealed features. In particular, we propose a self-supervised deep learning-based image separation approach that can be applied to the Xray images from such paintings to separate them into two hypothetical X-ray images. One of these reconstructed images is related to the X-ray image of the concealed painting, while the second one contains only information related to the X-ray image of the visible painting. The proposed separation network consists of two components: the analysis and the synthesis sub-networks. The analysis sub-network is based on learned coupled iterative shrinkage thresholding algorithms (LCISTA) designed using algorithm unrolling techniques, and the synthesis sub-network consists of several linear mappings. The learning algorithm operates in a totally self-supervised fashion without requiring a sample set that contains both the mixed X-ray images and the separated ones. The proposed method is demonstrated on a real painting with concealed content, Dona Isabel de Porcel ˜ by Francisco de Goya, to show its effectiveness.
| Type: | Article |
|---|---|
| Title: | Mixed X-Ray Image Separation for Artworks with Concealed Designs |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?pu... |
| 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: | Art Investigation, Image Separation, Deep Neural Networks, Convolutional Neural Networks, Unrolling technique |
| 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/10150252 |
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