Mixed X-Ray Image Separation for Artworks With Concealed Designs.
In: IEEE Transactions on Image Processing, Jg. 31 (2022-07-01), S. 4458-4473
Online
academicJournal
Zugriff:
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]
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Mixed X-Ray Image Separation for Artworks With Concealed Designs.
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Autor/in / Beteiligte Person: | Pu, Wei ; Huang, Jun-Jie ; Sober, Barak ; Daly, Nathan ; Higgitt, Catherine ; Daubechies, Ingrid ; Dragotti, Pier Luigi ; Rodrigues, Miguel R. D. |
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Zeitschrift: | IEEE Transactions on Image Processing, Jg. 31 (2022-07-01), S. 4458-4473 |
Veröffentlichung: | 2022 |
Medientyp: | academicJournal |
ISSN: | 1057-7149 (print) |
DOI: | 10.1109/TIP.2022.3185488 |
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