A Learning Based Approach to Separate Mixed X-Ray Images Associated with Artwork with Concealed Designs

Conference Paper

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 X-ray 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.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • January 1, 2021

Published In

Volume / Issue

  • 2021-August /

Start / End Page

  • 1491 - 1495

International Standard Serial Number (ISSN)

  • 2219-5491

International Standard Book Number 13 (ISBN-13)

  • 9789082797060

Digital Object Identifier (DOI)

  • 10.23919/EUSIPCO54536.2021.9616096

Citation Source

  • Scopus