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

Publication ,  Journal Article
Pu, W; Huang, J-J; Sober, B; Daly, N; Higgitt, C; Daubechies, I; Dragotti, PL; Rodrigues, MRD
Published in: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
January 2022

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.

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Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

January 2022

Volume

31

Start / End Page

4458 / 4473

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4607 Graphics, augmented reality and games
  • 4603 Computer vision and multimedia computation
  • 1702 Cognitive Sciences
  • 0906 Electrical and Electronic Engineering
  • 0801 Artificial Intelligence and Image Processing
 

Citation

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Pu, W., Huang, J.-J., Sober, B., Daly, N., Higgitt, C., Daubechies, I., … Rodrigues, M. R. D. (2022). Mixed X-Ray Image Separation for Artworks With Concealed Designs. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 31, 4458–4473. https://doi.org/10.1109/tip.2022.3185488
Pu, Wei, Jun-Jie Huang, Barak Sober, Nathan Daly, Catherine Higgitt, Ingrid Daubechies, Pier Luigi Dragotti, and Miguel R. D. Rodrigues. “Mixed X-Ray Image Separation for Artworks With Concealed Designs.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society 31 (January 2022): 4458–73. https://doi.org/10.1109/tip.2022.3185488.
Pu W, Huang J-J, Sober B, Daly N, Higgitt C, Daubechies I, et al. Mixed X-Ray Image Separation for Artworks With Concealed Designs. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2022 Jan;31:4458–73.
Pu, Wei, et al. “Mixed X-Ray Image Separation for Artworks With Concealed Designs.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, vol. 31, Jan. 2022, pp. 4458–73. Epmc, doi:10.1109/tip.2022.3185488.
Pu W, Huang J-J, Sober B, Daly N, Higgitt C, Daubechies I, Dragotti PL, Rodrigues MRD. Mixed X-Ray Image Separation for Artworks With Concealed Designs. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2022 Jan;31:4458–4473.

Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

January 2022

Volume

31

Start / End Page

4458 / 4473

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4607 Graphics, augmented reality and games
  • 4603 Computer vision and multimedia computation
  • 1702 Cognitive Sciences
  • 0906 Electrical and Electronic Engineering
  • 0801 Artificial Intelligence and Image Processing