1. Mixed X-Ray Image Separation for Artworks With Concealed Designs.
- Author
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Pu, Wei, Huang, Jun-Jie, Sober, Barak, Daly, Nathan, Higgitt, Catherine, Daubechies, Ingrid, Dragotti, Pier Luigi, and Rodrigues, Miguel R. D.
- Subjects
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X-ray imaging , *MACHINE learning , *SUPERVISED learning , *THRESHOLDING algorithms , *ARTIFICIAL neural networks , *LINEAR operators - Abstract
In this paper, we focus on X-ray images (X-radiographs) 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 X-ray 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, Do na Isabel de Porcel by Francisco de Goya, to show its effectiveness. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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