Mixed X-Ray Image Separation for Artworks With Concealed Designs.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Pu, W; Huang, J-J; Sober, B; Daly, N; Higgitt, C; Daubechies, I; Dragotti, PL; Rodrigues, MRD

Published Date

  • January 2022

Published In

Volume / Issue

  • 31 /

Start / End Page

  • 4458 - 4473

PubMed ID

  • 35763481

Electronic International Standard Serial Number (EISSN)

  • 1941-0042

International Standard Serial Number (ISSN)

  • 1057-7149

Digital Object Identifier (DOI)

  • 10.1109/tip.2022.3185488


  • eng