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

W. Pu, J. Huang, B. Sober, N. Daly, C. Higgitt, I. Daubechies, P.L. Dragotti, M.R.D. Rodrigues

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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
Original languageAmerican English
Pages (from-to)4458 - 4473
Number of pages16
JournalIEEE Transactions on Image Processing
StatePublished - 2022

Bibliographical note

Funding Information:
This work was supported in part by the Engineering and Physical Sciences Research Council under Grant EP/R032785/1 and in part by the Royal Society under Grant NIF/R1/180735.

Publisher Copyright:
© 1992-2012 IEEE.


  • Signal Processing and Analysis
  • Communication
  • Networking and Broadcast Technologies
  • Computing and Processing
  • X-ray imaging
  • Painting
  • Imaging
  • Image reconstruction
  • Feature extraction
  • Task analysis
  • Paints
  • Art investigation
  • image separation
  • deep neural networks
  • convolutional neural networks
  • unrolling technique


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