Abstract

To assess the accuracy of virtual cleaning of Old Master paintings (i.e. digital removal of discolored varnishes), a physical model was developed and experimentally tested using reflectance imaging spectroscopy on three paintings undergoing conservation treatment. The model predicts the reflectance spectra of the painting without varnish or after application of a new varnish from the reflectances of the painting with the aged varnish, given the absorption of the aged varnish and the scattering terms. The resulting color differences between the painting actually and virtually cleaned can approach the perceivable limit. Residual discrepancies are ascribable to spatial variations in the characteristics of the aged varnish (scattering, optical thickness) and the exposed painting (surface roughness).

© 2015 Optical Society of America

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References

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  1. P. Cotte and D. Dupraz, “Spectral imaging of Leonardo Da Vinci’s Mona Lisa: A true color smile without the influence of aged varnish,” in 3rd European Conference on Colour in Graphics, Imaging, and Vision (IS&T, 2006), pp. 311–317.
  2. G. Schirripa Spagnolo, “Virtual restoration: detection and removal of craquelure in digitized image of old paintings,” Proc. SPIE8084, O3A: Optics for Arts, Architecture, and Archaeology III, 80840B (2011).
    [Crossref]
  3. R. S. Berns, Rejuvenating the Appearance of Cultural Heritage Using Color and Imaging Science Techniques, in Proceedings of the 10th), Congress of the International Colour Association, J. L. Nieves and J. Hernández-Andrés, eds. (AIC, 2005), pp. 369–374.
  4. G. M. Cortelazzo, G. L. Geremia, and G. A. Mian, “Some results about Wiener-Volterra restoration of the original colour quality in old painting imagery,” in Proceedings of IEEE Workshop Nonlinear Signal Image Processing (NSIP, 1995), pp. 86–89.
  5. M. Pappas and I. Pitas, “Digital color restoration of old paintings,” IEEE Trans. Image Process. 9(2), 291–294 (2000).
    [Crossref]
  6. M. Barni, F. Bartolini, and V. Cappellini, “Image processing for virtual restoration of artworks,” IEEE Multimedia Mag. 7(2), 34–37 (2000).
    [Crossref]
  7. CIE, “CIE Colorimetry - Part 1: Standard Colorimetric Observers,” ISO 11664-1:2007(E)/CIE S 014-1/E:2006.
  8. P. Urban and R. R. Grigat, “Metamer density estimated color correction,” Signal Image Video Process. 3(171), 171–182 (2009).
    [Crossref]
  9. K. Martinez, J. Cupitt, and D. Saunders, “High-resolution colorimetric imaging of paintings,” Proc. SPIE 1901, 25 (1993).
    [Crossref]
  10. S. Baronti, A. Casini, F. Lotti, and S. Porcinai, “Multispectral imaging system for the mapping of pigments in works of art by use of principal-component analysis,” Appl. Opt. 37(8), 1299–1309 (1998).
    [Crossref]
  11. F. H. Imai and R. S. Berns, “High-resolution multi-spectral image capture for fine arts preservation,” in Proc. 4th Argentina Color Conference (1998), pp. 21–22.
  12. A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
    [Crossref]
  13. J. K. Delaney, J. G. Zeibel, M. Thoury, R. Littleton, M. Palmer, K. M. Morales, E. R. de la Rie, and A. Hoenigswald, “Visible and Infrared Imaging Spectroscopy of Picasso’s Harlequin Musician: Mapping and Identification of Artist Materials in Situ,” Appl. Spectrosc. 64(6), 584–594 (2010).
    [Crossref] [PubMed]
  14. T. B. Brill, Light: its interaction with art and antiquities (Plenum, 1980).
  15. E. R. de la Rie, “The influence of varnishes on the appearance of paintings,” Stud. Conserv. 32(1), 1–13 (1987).
    [Crossref]
  16. E. R. de la Rie, J. K. Delaney, K. M. Morales, C. A. Maines, and L. P. Sung, “Modification of Surface Roughness by Various Varnishes and Effect on Light Reflection,” Stud. Conserv. 55(2), 134–143 (2010).
    [Crossref]
  17. E. R. de la Rie, “Degradation and Stabilization of Varnishes for Paintings,” in Preprints to the 13th International Conference in the Stabilization and Degradation of Polymers, (1991), pp. 129–139.
  18. C. M. Palomero and M. Soriano, “Digital cleaning and ‘dirt’ layer visualization of an oil painting,” Opt. Express 19(21), 21011–21017 (2011).
    [Crossref] [PubMed]
  19. M. Bacci, A. Casini, C. Cucci, M. Picollo, B. Radicati, and M. Vervat, “Non-invasive spectroscopic measurements on the Il ritratto della figliastra by Giovanni Fattori: identification of pigments and colourimetric analysis,” J. Cult. Herit. 4, 329–336 (2003).
    [Crossref]
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  21. D. M. Conover, J. K. Delaney, and M. H. Loew, “Automatic registration and mosaicking of technical images of Old Master paintings,” Appl. Phys. A 119(4), 1567–1575 (2015).
    [Crossref]
  22. S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10(4), 210–218 (1985).
    [Crossref]
  23. R. S. Hunter, The measurement of appearance (J. Wiley & Sons, 1975).
  24. P. Kubelka, “New contributions to the optics of intensely light-scattering material - part I,” J. Opt. Soc. Am. 38, 448–457 (1948).
    [Crossref] [PubMed]
  25. J. H. Nobbs, “Kubelka-Munk theory and the prediction of reflectance,” Rev. Prog. Coloration 15, 66–75 (1985).
    [Crossref]
  26. R. L. Herbert, Georges Seurat, 1859–1891 (The Metropolitan Museum of Art, 1991), p. 405.
  27. CIE, “CIE Colorimetry - Part 4: 1976 L*a*b* Colour Space,” ISO 11664-4:2008(E)/CIE S 014-4/E:2007.

2015 (1)

D. M. Conover, J. K. Delaney, and M. H. Loew, “Automatic registration and mosaicking of technical images of Old Master paintings,” Appl. Phys. A 119(4), 1567–1575 (2015).
[Crossref]

2011 (1)

2010 (2)

2009 (1)

P. Urban and R. R. Grigat, “Metamer density estimated color correction,” Signal Image Video Process. 3(171), 171–182 (2009).
[Crossref]

2005 (1)

A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
[Crossref]

2003 (1)

M. Bacci, A. Casini, C. Cucci, M. Picollo, B. Radicati, and M. Vervat, “Non-invasive spectroscopic measurements on the Il ritratto della figliastra by Giovanni Fattori: identification of pigments and colourimetric analysis,” J. Cult. Herit. 4, 329–336 (2003).
[Crossref]

2000 (2)

M. Pappas and I. Pitas, “Digital color restoration of old paintings,” IEEE Trans. Image Process. 9(2), 291–294 (2000).
[Crossref]

M. Barni, F. Bartolini, and V. Cappellini, “Image processing for virtual restoration of artworks,” IEEE Multimedia Mag. 7(2), 34–37 (2000).
[Crossref]

1998 (1)

1993 (1)

K. Martinez, J. Cupitt, and D. Saunders, “High-resolution colorimetric imaging of paintings,” Proc. SPIE 1901, 25 (1993).
[Crossref]

1987 (1)

E. R. de la Rie, “The influence of varnishes on the appearance of paintings,” Stud. Conserv. 32(1), 1–13 (1987).
[Crossref]

1985 (2)

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10(4), 210–218 (1985).
[Crossref]

J. H. Nobbs, “Kubelka-Munk theory and the prediction of reflectance,” Rev. Prog. Coloration 15, 66–75 (1985).
[Crossref]

1948 (1)

Bacci, M.

A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
[Crossref]

M. Bacci, A. Casini, C. Cucci, M. Picollo, B. Radicati, and M. Vervat, “Non-invasive spectroscopic measurements on the Il ritratto della figliastra by Giovanni Fattori: identification of pigments and colourimetric analysis,” J. Cult. Herit. 4, 329–336 (2003).
[Crossref]

Barni, M.

M. Barni, F. Bartolini, and V. Cappellini, “Image processing for virtual restoration of artworks,” IEEE Multimedia Mag. 7(2), 34–37 (2000).
[Crossref]

Baronti, S.

Bartolini, F.

M. Barni, F. Bartolini, and V. Cappellini, “Image processing for virtual restoration of artworks,” IEEE Multimedia Mag. 7(2), 34–37 (2000).
[Crossref]

Berns, R. S.

F. H. Imai and R. S. Berns, “High-resolution multi-spectral image capture for fine arts preservation,” in Proc. 4th Argentina Color Conference (1998), pp. 21–22.

R. S. Berns, Rejuvenating the Appearance of Cultural Heritage Using Color and Imaging Science Techniques, in Proceedings of the 10th), Congress of the International Colour Association, J. L. Nieves and J. Hernández-Andrés, eds. (AIC, 2005), pp. 369–374.

Brill, T. B.

T. B. Brill, Light: its interaction with art and antiquities (Plenum, 1980).

Cappellini, V.

M. Barni, F. Bartolini, and V. Cappellini, “Image processing for virtual restoration of artworks,” IEEE Multimedia Mag. 7(2), 34–37 (2000).
[Crossref]

Casini, A.

A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
[Crossref]

M. Bacci, A. Casini, C. Cucci, M. Picollo, B. Radicati, and M. Vervat, “Non-invasive spectroscopic measurements on the Il ritratto della figliastra by Giovanni Fattori: identification of pigments and colourimetric analysis,” J. Cult. Herit. 4, 329–336 (2003).
[Crossref]

S. Baronti, A. Casini, F. Lotti, and S. Porcinai, “Multispectral imaging system for the mapping of pigments in works of art by use of principal-component analysis,” Appl. Opt. 37(8), 1299–1309 (1998).
[Crossref]

Conover, D. M.

D. M. Conover, J. K. Delaney, and M. H. Loew, “Automatic registration and mosaicking of technical images of Old Master paintings,” Appl. Phys. A 119(4), 1567–1575 (2015).
[Crossref]

Cortelazzo, G. M.

G. M. Cortelazzo, G. L. Geremia, and G. A. Mian, “Some results about Wiener-Volterra restoration of the original colour quality in old painting imagery,” in Proceedings of IEEE Workshop Nonlinear Signal Image Processing (NSIP, 1995), pp. 86–89.

Cotte, P.

P. Cotte and D. Dupraz, “Spectral imaging of Leonardo Da Vinci’s Mona Lisa: A true color smile without the influence of aged varnish,” in 3rd European Conference on Colour in Graphics, Imaging, and Vision (IS&T, 2006), pp. 311–317.

Cucci, C.

A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
[Crossref]

M. Bacci, A. Casini, C. Cucci, M. Picollo, B. Radicati, and M. Vervat, “Non-invasive spectroscopic measurements on the Il ritratto della figliastra by Giovanni Fattori: identification of pigments and colourimetric analysis,” J. Cult. Herit. 4, 329–336 (2003).
[Crossref]

Cupitt, J.

K. Martinez, J. Cupitt, and D. Saunders, “High-resolution colorimetric imaging of paintings,” Proc. SPIE 1901, 25 (1993).
[Crossref]

J. Kirby, D. Saunders, and J. Cupitt, “Colorants and Colour Change,” in Early Italian Painting Techniques and Analysis, T. Bakkenis, R. Hoppenbrouwers, and H. Dubois, eds. (Limburg Conservation Institute,2005), pp. 60–66.

de la Rie, E. R.

E. R. de la Rie, J. K. Delaney, K. M. Morales, C. A. Maines, and L. P. Sung, “Modification of Surface Roughness by Various Varnishes and Effect on Light Reflection,” Stud. Conserv. 55(2), 134–143 (2010).
[Crossref]

J. K. Delaney, J. G. Zeibel, M. Thoury, R. Littleton, M. Palmer, K. M. Morales, E. R. de la Rie, and A. Hoenigswald, “Visible and Infrared Imaging Spectroscopy of Picasso’s Harlequin Musician: Mapping and Identification of Artist Materials in Situ,” Appl. Spectrosc. 64(6), 584–594 (2010).
[Crossref] [PubMed]

E. R. de la Rie, “The influence of varnishes on the appearance of paintings,” Stud. Conserv. 32(1), 1–13 (1987).
[Crossref]

E. R. de la Rie, “Degradation and Stabilization of Varnishes for Paintings,” in Preprints to the 13th International Conference in the Stabilization and Degradation of Polymers, (1991), pp. 129–139.

Delaney, J. K.

D. M. Conover, J. K. Delaney, and M. H. Loew, “Automatic registration and mosaicking of technical images of Old Master paintings,” Appl. Phys. A 119(4), 1567–1575 (2015).
[Crossref]

J. K. Delaney, J. G. Zeibel, M. Thoury, R. Littleton, M. Palmer, K. M. Morales, E. R. de la Rie, and A. Hoenigswald, “Visible and Infrared Imaging Spectroscopy of Picasso’s Harlequin Musician: Mapping and Identification of Artist Materials in Situ,” Appl. Spectrosc. 64(6), 584–594 (2010).
[Crossref] [PubMed]

E. R. de la Rie, J. K. Delaney, K. M. Morales, C. A. Maines, and L. P. Sung, “Modification of Surface Roughness by Various Varnishes and Effect on Light Reflection,” Stud. Conserv. 55(2), 134–143 (2010).
[Crossref]

Dupraz, D.

P. Cotte and D. Dupraz, “Spectral imaging of Leonardo Da Vinci’s Mona Lisa: A true color smile without the influence of aged varnish,” in 3rd European Conference on Colour in Graphics, Imaging, and Vision (IS&T, 2006), pp. 311–317.

Geremia, G. L.

G. M. Cortelazzo, G. L. Geremia, and G. A. Mian, “Some results about Wiener-Volterra restoration of the original colour quality in old painting imagery,” in Proceedings of IEEE Workshop Nonlinear Signal Image Processing (NSIP, 1995), pp. 86–89.

Grigat, R. R.

P. Urban and R. R. Grigat, “Metamer density estimated color correction,” Signal Image Video Process. 3(171), 171–182 (2009).
[Crossref]

Herbert, R. L.

R. L. Herbert, Georges Seurat, 1859–1891 (The Metropolitan Museum of Art, 1991), p. 405.

Hoenigswald, A.

Hunter, R. S.

R. S. Hunter, The measurement of appearance (J. Wiley & Sons, 1975).

Imai, F. H.

F. H. Imai and R. S. Berns, “High-resolution multi-spectral image capture for fine arts preservation,” in Proc. 4th Argentina Color Conference (1998), pp. 21–22.

Kirby, J.

J. Kirby, D. Saunders, and J. Cupitt, “Colorants and Colour Change,” in Early Italian Painting Techniques and Analysis, T. Bakkenis, R. Hoppenbrouwers, and H. Dubois, eds. (Limburg Conservation Institute,2005), pp. 60–66.

Kubelka, P.

Littleton, R.

Loew, M. H.

D. M. Conover, J. K. Delaney, and M. H. Loew, “Automatic registration and mosaicking of technical images of Old Master paintings,” Appl. Phys. A 119(4), 1567–1575 (2015).
[Crossref]

Lotti, F.

A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
[Crossref]

S. Baronti, A. Casini, F. Lotti, and S. Porcinai, “Multispectral imaging system for the mapping of pigments in works of art by use of principal-component analysis,” Appl. Opt. 37(8), 1299–1309 (1998).
[Crossref]

Maines, C. A.

E. R. de la Rie, J. K. Delaney, K. M. Morales, C. A. Maines, and L. P. Sung, “Modification of Surface Roughness by Various Varnishes and Effect on Light Reflection,” Stud. Conserv. 55(2), 134–143 (2010).
[Crossref]

Martinez, K.

K. Martinez, J. Cupitt, and D. Saunders, “High-resolution colorimetric imaging of paintings,” Proc. SPIE 1901, 25 (1993).
[Crossref]

Mian, G. A.

G. M. Cortelazzo, G. L. Geremia, and G. A. Mian, “Some results about Wiener-Volterra restoration of the original colour quality in old painting imagery,” in Proceedings of IEEE Workshop Nonlinear Signal Image Processing (NSIP, 1995), pp. 86–89.

Morales, K. M.

Nobbs, J. H.

J. H. Nobbs, “Kubelka-Munk theory and the prediction of reflectance,” Rev. Prog. Coloration 15, 66–75 (1985).
[Crossref]

Palmer, M.

Palomero, C. M.

Pappas, M.

M. Pappas and I. Pitas, “Digital color restoration of old paintings,” IEEE Trans. Image Process. 9(2), 291–294 (2000).
[Crossref]

Picollo, M.

A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
[Crossref]

M. Bacci, A. Casini, C. Cucci, M. Picollo, B. Radicati, and M. Vervat, “Non-invasive spectroscopic measurements on the Il ritratto della figliastra by Giovanni Fattori: identification of pigments and colourimetric analysis,” J. Cult. Herit. 4, 329–336 (2003).
[Crossref]

Pitas, I.

M. Pappas and I. Pitas, “Digital color restoration of old paintings,” IEEE Trans. Image Process. 9(2), 291–294 (2000).
[Crossref]

Poggesi, M.

A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
[Crossref]

Porcinai, S.

A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
[Crossref]

S. Baronti, A. Casini, F. Lotti, and S. Porcinai, “Multispectral imaging system for the mapping of pigments in works of art by use of principal-component analysis,” Appl. Opt. 37(8), 1299–1309 (1998).
[Crossref]

Radicati, B.

A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
[Crossref]

M. Bacci, A. Casini, C. Cucci, M. Picollo, B. Radicati, and M. Vervat, “Non-invasive spectroscopic measurements on the Il ritratto della figliastra by Giovanni Fattori: identification of pigments and colourimetric analysis,” J. Cult. Herit. 4, 329–336 (2003).
[Crossref]

Saunders, D.

K. Martinez, J. Cupitt, and D. Saunders, “High-resolution colorimetric imaging of paintings,” Proc. SPIE 1901, 25 (1993).
[Crossref]

J. Kirby, D. Saunders, and J. Cupitt, “Colorants and Colour Change,” in Early Italian Painting Techniques and Analysis, T. Bakkenis, R. Hoppenbrouwers, and H. Dubois, eds. (Limburg Conservation Institute,2005), pp. 60–66.

Schirripa Spagnolo, G.

G. Schirripa Spagnolo, “Virtual restoration: detection and removal of craquelure in digitized image of old paintings,” Proc. SPIE8084, O3A: Optics for Arts, Architecture, and Archaeology III, 80840B (2011).
[Crossref]

Shafer, S. A.

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10(4), 210–218 (1985).
[Crossref]

Soriano, M.

Stefani, L.

A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
[Crossref]

Sung, L. P.

E. R. de la Rie, J. K. Delaney, K. M. Morales, C. A. Maines, and L. P. Sung, “Modification of Surface Roughness by Various Varnishes and Effect on Light Reflection,” Stud. Conserv. 55(2), 134–143 (2010).
[Crossref]

Thoury, M.

Urban, P.

P. Urban and R. R. Grigat, “Metamer density estimated color correction,” Signal Image Video Process. 3(171), 171–182 (2009).
[Crossref]

Vervat, M.

M. Bacci, A. Casini, C. Cucci, M. Picollo, B. Radicati, and M. Vervat, “Non-invasive spectroscopic measurements on the Il ritratto della figliastra by Giovanni Fattori: identification of pigments and colourimetric analysis,” J. Cult. Herit. 4, 329–336 (2003).
[Crossref]

Zeibel, J. G.

Appl. Opt. (1)

Appl. Phys. A (1)

D. M. Conover, J. K. Delaney, and M. H. Loew, “Automatic registration and mosaicking of technical images of Old Master paintings,” Appl. Phys. A 119(4), 1567–1575 (2015).
[Crossref]

Appl. Spectrosc. (1)

Color Res. Appl. (1)

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10(4), 210–218 (1985).
[Crossref]

IEEE Multimedia Mag. (1)

M. Barni, F. Bartolini, and V. Cappellini, “Image processing for virtual restoration of artworks,” IEEE Multimedia Mag. 7(2), 34–37 (2000).
[Crossref]

IEEE Trans. Image Process. (1)

M. Pappas and I. Pitas, “Digital color restoration of old paintings,” IEEE Trans. Image Process. 9(2), 291–294 (2000).
[Crossref]

J. Cult. Herit. (1)

M. Bacci, A. Casini, C. Cucci, M. Picollo, B. Radicati, and M. Vervat, “Non-invasive spectroscopic measurements on the Il ritratto della figliastra by Giovanni Fattori: identification of pigments and colourimetric analysis,” J. Cult. Herit. 4, 329–336 (2003).
[Crossref]

J. Opt. Soc. Am. (1)

Opt. Express (1)

Proc. SPIE (2)

A. Casini, M. Bacci, C. Cucci, F. Lotti, S. Porcinai, M. Picollo, B. Radicati, M. Poggesi, and L. Stefani, “Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi,” Proc. SPIE 5857, Optical Methods for Arts and Archaeology, 58570M (2005).
[Crossref]

K. Martinez, J. Cupitt, and D. Saunders, “High-resolution colorimetric imaging of paintings,” Proc. SPIE 1901, 25 (1993).
[Crossref]

Rev. Prog. Coloration (1)

J. H. Nobbs, “Kubelka-Munk theory and the prediction of reflectance,” Rev. Prog. Coloration 15, 66–75 (1985).
[Crossref]

Signal Image Video Process. (1)

P. Urban and R. R. Grigat, “Metamer density estimated color correction,” Signal Image Video Process. 3(171), 171–182 (2009).
[Crossref]

Stud. Conserv. (2)

E. R. de la Rie, “The influence of varnishes on the appearance of paintings,” Stud. Conserv. 32(1), 1–13 (1987).
[Crossref]

E. R. de la Rie, J. K. Delaney, K. M. Morales, C. A. Maines, and L. P. Sung, “Modification of Surface Roughness by Various Varnishes and Effect on Light Reflection,” Stud. Conserv. 55(2), 134–143 (2010).
[Crossref]

Other (12)

E. R. de la Rie, “Degradation and Stabilization of Varnishes for Paintings,” in Preprints to the 13th International Conference in the Stabilization and Degradation of Polymers, (1991), pp. 129–139.

T. B. Brill, Light: its interaction with art and antiquities (Plenum, 1980).

R. S. Hunter, The measurement of appearance (J. Wiley & Sons, 1975).

F. H. Imai and R. S. Berns, “High-resolution multi-spectral image capture for fine arts preservation,” in Proc. 4th Argentina Color Conference (1998), pp. 21–22.

CIE, “CIE Colorimetry - Part 1: Standard Colorimetric Observers,” ISO 11664-1:2007(E)/CIE S 014-1/E:2006.

P. Cotte and D. Dupraz, “Spectral imaging of Leonardo Da Vinci’s Mona Lisa: A true color smile without the influence of aged varnish,” in 3rd European Conference on Colour in Graphics, Imaging, and Vision (IS&T, 2006), pp. 311–317.

G. Schirripa Spagnolo, “Virtual restoration: detection and removal of craquelure in digitized image of old paintings,” Proc. SPIE8084, O3A: Optics for Arts, Architecture, and Archaeology III, 80840B (2011).
[Crossref]

R. S. Berns, Rejuvenating the Appearance of Cultural Heritage Using Color and Imaging Science Techniques, in Proceedings of the 10th), Congress of the International Colour Association, J. L. Nieves and J. Hernández-Andrés, eds. (AIC, 2005), pp. 369–374.

G. M. Cortelazzo, G. L. Geremia, and G. A. Mian, “Some results about Wiener-Volterra restoration of the original colour quality in old painting imagery,” in Proceedings of IEEE Workshop Nonlinear Signal Image Processing (NSIP, 1995), pp. 86–89.

R. L. Herbert, Georges Seurat, 1859–1891 (The Metropolitan Museum of Art, 1991), p. 405.

CIE, “CIE Colorimetry - Part 4: 1976 L*a*b* Colour Space,” ISO 11664-4:2008(E)/CIE S 014-4/E:2007.

J. Kirby, D. Saunders, and J. Cupitt, “Colorants and Colour Change,” in Early Italian Painting Techniques and Analysis, T. Bakkenis, R. Hoppenbrouwers, and H. Dubois, eds. (Limburg Conservation Institute,2005), pp. 60–66.

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Figures (6)

Fig. 1
Fig. 1 Diagram illustrating a cross-section of a painting (the substrate of pigment/binder matrix) covered by semi-transparent layer of degraded varnish (left), uncovered (center) and covered by a fresh transparent varnish (right).
Fig. 2
Fig. 2 “Still Life” by Willem Kalf, c. 1660 (National Gallery of Art, Washington DC -Chester Dale collection). The red spot in the color image of the painting (inside the delftware bowl) indicates where the multiple diffuse reflectance measurements were made.
Fig. 3
Fig. 3 In-situ single-pass transmission spectra of the aged varnish layer (including ‘grime’) calculated from the diffuse reflectance measurements (solid lines) and best fit scaled transmission of the re-solubilized varnish (dashed lines) for the two paintings examined.
Fig. 4
Fig. 4 G. Seurat, ‘Haymakers at Montfermeil’, c. 1882 (National Gallery of Art, Washington DC - Collection of Mr. and Mrs. Paul Mellon). Left: color images of painting before (top) and after physical removal of aged varnish (center), and predicted appearance with virtual cleaning (bottom). Right: ΔE between the images before and after actual cleaning (top) and between the images after actual and virtual cleaning (bottom). The 1/3 on the right of the painting had already been cleaned and acted as a ‘control region’.
Fig. 5
Fig. 5 Detail of ‘Flowers in an urn’ by J. Van Huysum, c. 1721 (National Gallery of Art, Washington DC - Adolph Caspar Miller Fund). Color image of the cleaned, unvarnished painting and reflectance spectra measured with RIS before and after varnish removal (RUC(λ) and RC(λ)) and the reflectance predicted with virtual cleaning (RVC(λ)) for six selected points.
Fig. 6
Fig. 6 Detail of ‘Flowers in an urn’ by J. Van Huysum, c. 1721 (National Gallery of Art, Washington DC - Adolph Caspar Miller Fund). From left: color images of the painting with the aged varnish (1), after removal of the aged varnish and fresh varnishing (2), and after virtual cleaning aiming to match the freshly varnished painting (3). On the right: ΔE between the predicted and measured colorimetric values - the areas highlighted in red correspond to old inpaintings that were removed during the treatment.

Tables (2)

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Table 1 Values of the variables obtained in the experimental analysis.

Tables Icon

Table 2 Color change with treatment and results of virtual cleaning predicting the appearance of the painting after physical varnish removal (first two rows) and after application of a new varnish (last two rows). The experimental error is ΔE=1.8.

Equations (9)

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R C ( λ ) = R P i + R P b ( λ ) .
R U C ( λ ) = R V b + T 2 ( λ ) R P b ( λ ) 1 R V b R P b ( λ ) .
R P b ( λ ) = R U C ( λ ) R V b T 2 ( λ ) + R V b ( R U C ( λ ) R V b ) .
R C ( λ ) = R U C ( λ ) R V b T 2 ( λ ) + R V b ( R U C ( λ ) R V b ) + R P i .
R V b = R U C b l a c k ( λ 0 ) a n d R P i = R C b l a c k ( λ 0 ) .
T ( λ ) = [ R U C w h i t e ( λ ) R U C b l a c k ( λ 0 ) R C w h i t e ( λ ) R C b l a c k ( λ 0 ) R U C b l a c k ( λ 0 ) ( R U C w h i t e ( λ ) R U C b l a c k ( λ 0 ) ) ] 1 / 2 .
T ( λ ) = ( 1 G ) T var n i s h ( λ ) ,
T var n i s h ( λ ) = T s o l ( λ ) α
R V C ( x , y ) ( λ ) = R U C ( x , y ) ( λ ) R V b T 2 ( λ ) + R V b ( R U C ( x , y ) ( λ ) R V b ) + R P i .

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