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Digital Cultural Heritage Imaging via Osmosis Filtering

  • Simone Parisotto
  • Luca Calatroni
  • Claudia Daffara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)

Abstract

In Cultural Heritage (CH) imaging, data acquired within different spectral regions are often used to inspect surface and sub-surface features. Due to the experimental setup, these images may suffer from intensity inhomogeneities, which may prevent conservators from distinguishing the physical properties of the object under restoration. Furthermore, in multi-modal imaging, the transfer of information between one modality to another is often used to integrate image contents.

In this paper, we apply the image osmosis model proposed in [4, 10, 12] to solve correct these problems arising when diagnostic CH imaging techniques based on reflectance, emission and fluorescence mode in the optical and thermal range are used. For an efficient computation, we use stable operator splitting techniques to solve the discretised model. We test our methods on real artwork datasets: the thermal measurements of the mural painting “Monocromo” by Leonardo Da Vinci, the UV-VIS-IR imaging of an ancient Russian icon and the Archimedes Palimpsest dataset.

Keywords

Osmosis filtering Operator splitting Thermal-Quasi Reflectography UV-IR imaging Multi-modal imaging Cultural Heritage 

Notes

Acknowledgements

SP acknowledges UK EPSRC grant EP/L016516/1. LC acknowledges the support of the Fondation Mathématique Jacques Hadamard (FMJH). The diagnostics were supported by Dr. Francesca Tasso (Soprintendenza of Castello Sforzesco) and Vittorio Barra (University of Verona).

Data Statement

The “Monocromo” data are sensitive: restricted access is subjected to the approval of “Soprintendenza Castello, Musei Archeologici e Musei Storici”, Milan. The Archimede palimpsest is released under CC-BY 3.0 license, http://openn.library.upenn.edu/Data/0014/ArchimedesPalimpsest/.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Simone Parisotto
    • 1
  • Luca Calatroni
    • 2
  • Claudia Daffara
    • 3
  1. 1.CCAUniversity of CambridgeCambridgeUK
  2. 2.CMAP, École PolytechniquePalaiseau CedexFrance
  3. 3.University of VeronaVeronaItaly

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