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

Wei Pu, Junjie Huang, Barak Sober, Nathan Daly, Catherine Higgitt, Pier Luigi Dragotti, Ingrid Daubechies, Miguel R.D. Rodrigues

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


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.

Original languageAmerican English
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9789082797060
StatePublished - 2021
Externally publishedYes
Event29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference29th European Signal Processing Conference, EUSIPCO 2021

Bibliographical note

Publisher Copyright:
© 2021 European Signal Processing Conference. All rights reserved.


  • Art investigation
  • Convolutional neural networks
  • Deep neural networks
  • Image separation


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