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 languageEnglish
Title of host publication2021 29th European Signal Processing Conference (EUSIPCO)
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9789082797060
StatePublished - 2021
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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