Hypercolorimetric multispectral imaging system for cultural heritage diagnostics: an innovative study for copper painting examination

  • C. Colantonio
  • C. PelosiEmail author
  • L. D’Alessandro
  • S. Sottile
  • G. Calabrò
  • M. Melis
Regular Article
Part of the following topical collections:
  1. Focus Point on Past and Present: Recent Advances in the Investigation of Ancient Materials by Means of Scientific Instrumental Techniques


The aim of this work is to test the application of a new multispectral imaging system, named Hypercolorimetric Multispectral Imaging, on two little 17th century oil paintings on copper in order to support the restoration activities. Hypercolorimetric Multispectral Imaging is a non-invasive, rapid and diagnostic technique that allows in situ accurate and reproducible spectral reflectance measurements between 300nm and 1000nm to obtain seven monochromatic very high spatial resolution images (36 megapixels starting from RAW format). The acquired images are transformed into radiometric and colorimetric measurements, consisting of 7 monochromatic images of spectral reflectance and one colorimetric image. All these calibrated images constitute the base for further processing performed through a dedicated software that implements a number of functions. In the present paper, a subset of those functions has been used. Specifically: Principal Component Analysis, spectral clustering, spectral mapping, multiband contrast enhancement and edge detection. Combining calibrated images of different spectral regions acquisitions, it was possible to extract relevant information about the state of conservation of the two copper paintings and further significant details were readable compared with the data coming from each single acquisition. The Hypercolorimetric Multispectral Imaging acquisition process revealed to be fast allowing to be performed during the cleaning stage of the paintings. The imaging nature of the analysis allowed to compare and map different areas of the surfaces producing degradation maps of the painting layers, which represents a precious decision-making tool for conservators.


  1. 1.
    I. Horovitz, The Conservator 10, 44 (1986)CrossRefGoogle Scholar
  2. 2.
    D.A. Scott, Copper and Bronze in Art: Corrosion, Colorants, and Conservation (J. Paul Getty Museum Pubs, Los Angeles, 2002)Google Scholar
  3. 3.
    M.L. Oliveira, A technical investigation of an oil painting on copper support, including a story on consolidants for treatment, Master Thesis, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (2015)Google Scholar
  4. 4.
    D. Vega, I.P. Cardoso, L. Carlyle, Conserv. Patrim. 27, 23 (2018)CrossRefGoogle Scholar
  5. 5.
    E.P. Bowron, A brief history of European oil paintings on copper, 1560–1775, in Copper as Canvas: Two Centuries of Masterpiece Paintings on Copper, 1575–1775, edited by M. Komanecky (Oxford University Press, Phoenix Art Museum, New York, 1999) pp. 9--30Google Scholar
  6. 6.
    L.C. Palopoulou, D. Watkinson, Rev. Conserv. 7, 65 (2006)Google Scholar
  7. 7.
    K. Martinez, J. Cupitt, D. Saunders, R. Pillay, Proc. IEEE 90, 28 (2002)CrossRefGoogle Scholar
  8. 8.
    A. Polak, T. Kelmana, P. Murray, S. Marshall, D.J.M. Stothard, N. Eastaugh, F. Eastaugh, J. Cult. Herit. 26, 1 (2017)CrossRefGoogle Scholar
  9. 9.
    A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, L. Stefani, Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi, in Proceedings of SPIE5857, Optical Methods for Arts and Archaeology, Munich, 2005, edited by R. Salimbeni, L. Pezzati, Vol. 585 (SPIE, Washington, 2005)Google Scholar
  10. 10.
    F. Daniel, A. Mounier, J. Pérez-Arantegui, C. Pardos, N. Prieto-Taboada, S.F.-O.d. Vallejuelo, K. Castro, Microchem. J. 126, 113 (2016)CrossRefGoogle Scholar
  11. 11.
    J. Dyer, G. Verri, J. Cupitt, Multispectral Imaging in Reflectance and Photo-induced Luminescence modes: A User Manual (The British Museum, London, 2013) Version 1.0Google Scholar
  12. 12.
    A. Cosentino, Archeomatica 2, 12 (2015)Google Scholar
  13. 13.
    A. Cosentino, e-Preserv. Sci. 12, 1 (2015)Google Scholar
  14. 14.
    A. Cosentino, J. Conserv. Museum Stud. 13, 1 (2015)CrossRefGoogle Scholar
  15. 15.
    H. Liang, D. Saunders, J. Cupitt, J. Imag. Sci. Technol. 49, 551 (2005)Google Scholar
  16. 16.
    A. Pelagotti, A. Del Mastio, A. De Rosa, A. Piva, IEEE Signal Process. Mag. 25, 27 (2008)ADSCrossRefGoogle Scholar
  17. 17.
    R. Sitnik, J.F. Krzeslowski, G. Maczkowski, Opt. Eng. 51, 021115 (2012)ADSCrossRefGoogle Scholar
  18. 18.
    C. Fischer, I. Kakoulli, Stud. Conserv. 51, 3 (2006)CrossRefGoogle Scholar
  19. 19.
    D. Lau, C. Villis, S. Furmanc, M. Livetta, Anal. Chim. Acta 610, 15 (2008)CrossRefGoogle Scholar
  20. 20.
    A. Casini, F. Lotti, M. Picollo, L. Stefani, E. Buzzegoli, Stud. Conserv. 44, 39 (1999)Google Scholar
  21. 21.
    R.C. Gonzalez, R.E. Woods, Digital Image Processing, 3rd edition (Prentice Hall, Upper Saddle River, N.J., 2008)Google Scholar
  22. 22.
    G. Capobianco, L. Calienno, C. Pelosi, M. Scacchi, G. Bonifazi, G. Agresti, R. Picchio, U. Santamaria, S. Serranti, A. Lo Monaco, Spectrochim. Acta A 172, 34 (2017)ADSCrossRefGoogle Scholar
  23. 23.
    C. Pelosi, G. Capobianco, G. Agresti, G. Bonifazi, F. Morresi, S. Rossi, U. Santamaria, S. Serranti, Spectrochim. Acta A 198, 92 (2018)ADSCrossRefGoogle Scholar
  24. 24.
    M. Melis, M. Miccoli, D. Quarta, Multispectral hypercolorimetry and automatic guided pigment identification: some masterpieces case studies, in Proceedings of SPIE 8790, Optics for Arts, Architecture, and Archaeology IV, Munich, 2013, edited by L. Pezzati, P. Targowski, Vol. 8790 (SPIE, Washington, 2013)Google Scholar
  25. 25.
    A. Stock, Preparing a copper panel for painting: a late sixteenth-century reconstruction, in Artists’ Footsteps - The Reconstruction of Pigments and Paintings, edited by L. Wrapson, J. Rose, R. Miller, B. Bucklow (Archetype Publications, London, 2012) pp. 197--202Google Scholar
  26. 26.
    L. Fuster Lopez, Paintings on copper (and other metal plates): production, degradation and conservation issues, in Proceedings of the symposium held at the Universitat Politècnica de València, Valencia, Spain, January 27–28 (2017)Google Scholar
  27. 27.
    CEN EN17138, Conservation of cultural heritage - Methods and materials for cleaning porous inorganic materials (CEN, Brussels, 2017)Google Scholar
  28. 28.
    M. Miccoli, M. Melis, Multispectral light metering system for cultural heritage diagnosis and conservation, in Proceedings of SPIE 8790, Optics for Arts, Architecture, and Archaeology IV, Munich, 2013, edited by L. Pezzati, P. Targowski, Vol. 8790 (SPIE, Washington, 2013),Google Scholar
  29. 29.
    M. Melis, M. Miccoli, Trasformazione evoluzionistica di una fotocamera reflex digitale in un sofisticato strumento per misure fotometriche e colorimetriche, in Colore e Colorimetria contributi multidisciplinari, edited by M. Rossi, A. Siniscalco, Vol. IX A (Maggioli Editore, Santarcangelo di Romagna, 2013) pp. 28--38Google Scholar
  30. 30.
    M. Samarelli, M. Melis, M. Miccoli, E. Egarter Vigl, A.R. Zink, J. Cult. Herit. 16, 753 (2015)CrossRefGoogle Scholar
  31. 31.
    M. Barni, A. Pelagotti, A. Piva, IEEE Sign. Proc. Mag. 22, 141 (2005)ADSCrossRefGoogle Scholar
  32. 32.
    P. Abry, A.G. Klein, W.A. Sethares, C.R. Johnson Jr., IEEE Signal Process Mag. 16, 14 (2015)ADSCrossRefGoogle Scholar

Copyright information

© Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Engineering for Energy and Environment, DEIm Dept.University of TusciaViterboItaly
  2. 2.Laboratory of Diagnostics and Materials Science, DEIm Dept.University of TusciaViterboItaly
  3. 3.Restoration Laboratory, DIBAF Dept.University of TusciaViterboItaly
  4. 4.Profilocolore S.r.l.RomaItaly

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