Multispectral Imaging and Digital Restoration for Paintings Documentation

  • Marco Landi
  • Giuseppe Maino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6979)


Spectral imaging for radiation wavelengths different from the visible ones, namely in the infrared (IR) and ultraviolet (UV) ranges provides useful information about the actual preservation state and past conditions of paintings. As a consequence, it is possible to combine this information with that obtained in the usual RGB visible basis and to propose digital or ’virtual’ restoration of a painting, taking into account its history, modifications and repaintings done in the past. As an example, a work of Pietro Lianori is discussed and analysed.


Multispectral analysis IR and UV images virtual restoration painting 


  1. 1.
    Gonzales, C., Woods, E.: Digital Image Processing. Addison-Wesley, N.Y (1993)Google Scholar
  2. 2.
    Bonifazzi, C., Ferriani, S., Maino, G., Tartari, A.: Multispectral examination of paintings and works of art: A principal component analysis approach. In: Proceedings of CLADAG 2003 Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLUEB, Bologna, pp. 67–70 (2003)Google Scholar
  3. 3.
    Bonifazzi, C., Ferriani, S., Romano, A., Maino, G., Tartari, A.: Multispectral Examination of Paintings: A Principal Component Image Analysis Approach. In: Proceedings of ART 2005 – 8th International Conference on Non-Destructive Investigations and Microanalysis for the Diagnostics and Conservation of the Cultural and Environmental Heritage, Lecce, May 15-19 (2005)Google Scholar
  4. 4.
    Maino, G., Bruni, S., Ferriani, S., Musumeci, A., Visparelli, D.: Multispectral analysis of paintings and wooden sculptures. In: Proceedings of II Congresso Nazionale AIAr Scienza e Beni Culturali, Patron Editore, Bologna, pp. 203–214 (2002)Google Scholar
  5. 5.
    MUSIS 2007 MultiSpectral Imaging System, Operational Manual, Art Innovation (2007)Google Scholar
  6. 6.
    Geladi, P., Grahn, H.: Multivariate Image Analysis. Wiley, N.Y (1996)Google Scholar
  7. 7.
    Baronti, S., et al.: In: Del Bimbo, A. (ed.) Image Analysis and Processing, Springer, Heidelberg (1997)Google Scholar
  8. 8.
    Geladi, P.: Chemometrics in spectroscopy I: Classical chemometrics. Spectro-chimica Acta B 58, 767–782 (2003)CrossRefGoogle Scholar
  9. 9.
    Huang, J., et al.: Multi-way method in image analysis: Relationships and applications. Chemometrics and Intelligent. Laboratory System 66, 203–252 (2003)CrossRefGoogle Scholar
  10. 10.
    Jackson, J.E.: A User Guide to Principal Components. Wiley Interscience, Hoboken (1992)Google Scholar
  11. 11.
    Kiers, H.A.L.: Towards a standardized notation and terminology in multiway analysis. Journal of Chemometrics 14, 105–122 (2000)CrossRefGoogle Scholar
  12. 12.
    Biagi Maino, D., Grimaldi, E., Maino, G.: Analisi multispettrali su un dipinto di Pietro Lianori. Archeomatica, 16–20 (2009)Google Scholar
  13. 13.
    Brandi, C.: Teoria del restauro. Edizioni di Storia e Letteratura, Roma (1963)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marco Landi
    • 1
  • Giuseppe Maino
    • 1
    • 2
  1. 1.Faculty of Preservation of the Cultural HeritageUniversity of BolognaRavennaItaly
  2. 2.Energy and Sustainable Economic DevelopmentENEA: Italian National Agency for New TechnologiesBolognaItaly

Personalised recommendations