Advertisement

Mathematical Models for Restoration of Baroque Paintings

  • Pantaleón D. Romero
  • Vicente F. Candela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)

Abstract

In this paper we adapt different techniques for image deconvolution, to the actual restoration of works of arts (mainly paintings and sculptures) from the baroque period. We use the special characteristics of these works in order to both restrict the strategies and benefit from those properties.

We propose an algorithm which presents good results in the pieces we have worked. Due to the diversity of the period and the amount of artists who made it possible, the algorithms are too general even in this context. This is a first approach to the problem, in which we have assumed very common and shared features for the works of art. The flexibility of the algorithm, and the freedom to choose some parameters make it possible to adapt the problem to the knowledge that restorators in charge may have about a particular work.

Keywords

Coarse Scale Actual Restoration Local Linearization Blind Deconvolution Deconvolved Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Candela, V., Marquina, A., Serna, S.: A Local Spectral Inversion of a Linearized TV Model for Denoising an Deblurring. IEEE Transactions on Image Processing 12(7), 808–816 (2003)CrossRefGoogle Scholar
  2. 2.
    Carasso, A.: Direct Blind Deconvolution. SIAM J. Numer. Anal. 61, 1980–2007 (2001)MATHMathSciNetGoogle Scholar
  3. 3.
    Osher, S.J., Sethian, J.A.: Fronts propagation with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of computacional physics 79, 12–49 (1988)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine inteligence 12 (1990)Google Scholar
  5. 5.
    Rudin, L., Osher, S.: Total variation based image restoration with free local constraints. In: Proc. IEEE Internat. Conf. Imag. Proc., pp. 31–35 (1994)Google Scholar
  6. 6.
    Marquina, A., Osher, S.: Explicit algorithms for a new time dependent model based on level set motion for nonlinear deblurring and noise removal. SIAM J. Sci. Comput. 22, 387–405 (2000)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Tichonov, A., Arsenin, V.: Solution of ill-posed problems. Wiley, New York (1977)Google Scholar
  8. 8.
    Catté, F., Lions, P.L., Morel, J.M., Coll, T.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 29, 182–193 (1992)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Martucci, S.A.: Symmetric convolution and the discrete sine and cosine transforms. IEEE Trans. Signal Processing 42, 1038–1051 (1994)CrossRefGoogle Scholar
  10. 10.
    Bergeon, S.: Colour et restauration. Techne 4, 17–28 (1976)Google Scholar
  11. 11.
    Wyszecky, G., Stiles, W.S.: Color Science, p. 372. John Wiley & Sons, New York (1982)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pantaleón D. Romero
    • 1
  • Vicente F. Candela
    • 1
  1. 1.Departament of Applied Maths.University of ValenciaBurjassot

Personalised recommendations