Computer-Aided Reclamation of Lost Art

  • Maria Lena Demetriou
  • Jon Yngve Hardeberg
  • Gabriel Adelmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)


There are numerous approaches towards restoration of art, including computer applications as aid to manual performance. However, to our knowledge, it has not been attempted to recuperate high quality images of missing or presumably destroyed works of art. While these works will never again be available in their original form, it may be feasible to considerably enhance the quality of preserved photographic reproductions. A pioneering combination of super-resolution and colour correction is presented here, targeting the reclamation of high quality images of lost works of art. The techniques are performed by example, utilising correspondence between artworks of similar nature, currently available both in low and high quality. With extensive prior knowledge in the domains of super-resolution and colour correction, selected approaches were studied, implemented and tested, concluding to the most efficient. Experimental results are highly promising, revealing a new research path in colour imaging for fine art.


Fine Art Restoration Super-Resolution Colour Correction 


  1. 1.
    Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning Low-Level Vision. International Journal of Computer Vision 40(1), 25–47 (2000)zbMATHCrossRefGoogle Scholar
  2. 2.
    Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comp. Graph. and Appl. 22(2), 56–65 (2002)CrossRefGoogle Scholar
  3. 3.
    Yang, J., Wright, J., Ma, Y., Huang, T.: Image super-resolution as sparse-representation of raw image patches. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1–8 (2008)Google Scholar
  4. 4.
    Zeyde, R., Elad, M., Protter, M.: On Single Image Scale-Up Using Sparse-Representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2011. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Wong, P.W.: Inverse halftoning and kernel estimation for error diffusion. IEEE Transactions on Image Processing 4(4), 486–498 (1995)CrossRefGoogle Scholar
  6. 6.
    Minami, Y., Azuma, S.-I., Sugie, T.: An inverse halftoning algorithm based on super-resolution image reconstruction. In: Proceedings of SICE Annual Conference 2010, pp. 1110–1113 (August 2010)Google Scholar
  7. 7.
    Xu, W., Mulligan, J.: Performance evaluation of color correction approaches for automatic multi-view image and video stitching. In: IEEE Int. Conference on Computer Vision and Pattern Recognition, San Francisco, USA, pp. 263–270 (2010)Google Scholar
  8. 8.
    Xiao, X., Ma, L.: Color transfer in correlated color space. In: Proc. ACM International Conference on Virtual Reality Continuum and Its Applications, pp. 305–309 (2006)Google Scholar
  9. 9.
    Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Computer Graphics and Applications 21(5), 34–41 (2001)CrossRefGoogle Scholar
  10. 10.
    Brown, M., Lowe, D.G.: Recognising panoramas. In: Proc. ICCV, vol. 2, pp. 1218–1225 (2003)Google Scholar
  11. 11.
    Fecker, U., Barkowsky, M., Kaup, A.: Histogram-based prefiltering for luminance and chrominance compensation of multiview video. IEEE Transactions on Circuits and Systems for Video Technology 18(9), 1258–1267 (2008)CrossRefGoogle Scholar
  12. 12.
    Elad, M., Aharon, M.: Image denoising via learned dictionaries and sparse representation. In: CVPR, pp. 895–900 (2006)Google Scholar
  13. 13.
    Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing 15(12), 3736–3745 (2006)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Aharon, M., Elad, M., Bruckstein, A.: The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans. on Signal Processing 54(11), 4311–4322 (2006)CrossRefGoogle Scholar
  15. 15.
    Elad, M., Rubinstein, R., Zibulevsky, M.: Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit. Technical report, CS, Technion (April 2008)Google Scholar
  16. 16.
    Ruderman, D., Cronin, T., Chiao, C.: Statistics of cone responses to natural images: Implications for visual coding. J. Optical Soc. of America 15(8), 2036–2045 (1998)CrossRefGoogle Scholar
  17. 17.
    Dulberg, F.: Rubens. E.A. Seeman, Germany (1932)Google Scholar
  18. 18.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  19. 19.
    Zhang, X., Wandell, B.A.: A spatial extension of cielab for digital color image reproduction. In: SID Symposium Technical Digest, vol. 27, pp. 731–734 (1996)Google Scholar
  20. 20.
    Anderson, H.S., Gupta, M.R.: Joint deconvolution and imaging. In: Proc. SPIE Conf. on Computational Imaging, vol. 7246, pp. 72460C–72460C-12 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maria Lena Demetriou
    • 1
  • Jon Yngve Hardeberg
    • 1
  • Gabriel Adelmann
    • 1
  1. 1.The Norwegian Colour and Visual Computing LaboratoryGjøvik University CollegeGjøvikNorway

Personalised recommendations