Removing Line Scratches in Digital Image Sequences by Fusion Techniques

  • Giuliano Laccetti
  • Lucia Maddalena
  • Alfredo Petrosino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


Many algorithms have been proposed in literature for digital film restoration; unfortunately, none of them ensures a perfect restoration whichever is the image sequence to be restored. Here, we propose an approach to digital scratch restoration based on image fusion techniques for combining relatively well settled distinct techniques. Qualitative results are deeply investigated for several real image sequences.


Fusion Technique Aggregation Operator Visual Module Removal Algorithm Image Fusion Technique 
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.


  1. 1.
    Acton, S.T., Mukherjee, D.P., Havlicek, J.P., Bovik, A.C.: Oriented Texture Completion by AM-FM Reaction-Diffusion. IEEE Transactions on Image Processing 10, 885–896 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image Inpainting. Computer Graphics, 417-424 (2000)Google Scholar
  3. 3.
    Bornard, R., Lecan, E., Laborelli, L., Chenot, J.-H.: Missing Data Correction in Still Images and Image Sequences. In: Proc. ACM Multimedia 2002, Juan-les-Pins, France, pp. 355–361 (2002)Google Scholar
  4. 4.
    Chan, T.F., Shen, J.: Mathematical Models for Local Non-Texture Inpaintings. UCLA CAM Report n. 00-11 (2000)Google Scholar
  5. 5.
    Decenciere Ferrandiere, E.: Restauration Automatique de Films Anciens. PhD Thesis, Ecole Nationale Superieure des Mines de Paris (1997)Google Scholar
  6. 6.
    Isgró, F., Tegolo, D.: A distributed genetic algorithm for restoration of vertical line scratches. Parallel Computing (accepted for publication)Google Scholar
  7. 7.
    Joyeux, L., Boukir, S., Besserer, B.: Tracking and MAP Reconstruction of Line Scratches in Degraded Motion Pictures. Machine Vision and Applications 13, 119–128 (2002)CrossRefGoogle Scholar
  8. 8.
    Kao, O., Engehausen, J.: Scratch Removal in Digitised Film Sequences. In: Proc. International Conference on Imaging Science, Systems, and Technology (CISST), pp. 171–179 (2000)Google Scholar
  9. 9.
    Kokaram, A.C.: Motion Picture Restoration: Digital Algorithms for Artefacts Suppression in Archived Film and Video. Springer, Heidelberg (1998)Google Scholar
  10. 10.
    Machì, A., Collura, F., Nicotra, F.: Detection of Irregular Linear Scratches in Aged Motion Picture Frames and Restoration using Adaptive Masks. In: Proc. IASTED Int. Conf. SIP 2002, Kawai, Usa, pp. 254–259 (2002)Google Scholar
  11. 11.
    Maddalena, L.: Efficient Methods for Scratch Removal in Image Sequences. In: Proc. 11th International Conference on Image Analysis and Processing (ICIAP2001), pp. 547–552. IEEE Computer Society, Los Alamitos (2001)CrossRefGoogle Scholar
  12. 12.
    Morris, R.D.: Image Sequence Restoration Using Gibbs Distributions. PhD Thesis, University of Cambridge (1995)Google Scholar
  13. 13.
    Saito, T., Komatsu, T., Ohuchi, T., Seto, T.: Image Processing for Restoration of Heavily-Corrupted Old Film Sequences. In: Proc. ICPR 2000, pp. 3017–3020. IEEE, Barcellona (2000)Google Scholar
  14. 14.
    Bloch, I.: Information Combination Operators for Data Fusion: A Comparative Review with Classification. IEEE Transactions on Systems, Man, Cybernetics 26, 52–67 (1996)CrossRefGoogle Scholar
  15. 15.
    Ho, T.K., Hull, J.J., Srihari, S.N.: Decision Combination in Multiple Classifier Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 66–75 (1994)Google Scholar
  16. 16.
    Laccetti, G., Maddalena, L., Petrosino, A.: Parallel/Distributed Film Line Scratch Restoration by Fusion Techniques. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3044, pp. 524–534. Springer, Heidelberg (2004)Google Scholar
  17. 17.
    Perrone, M.P., Cooper, L.N.: When networks disagree: Ensemble method for neural networks. In: Mammone, R.J. (ed.) Artificial Neural Networks for Speech and Vision, pp. 126–142. Chapman & Hall, New York (1993)Google Scholar
  18. 18.
    Roli, F.: Linear Combiners for Fusion of Pattern Classifiers. In: Int. School on Neural Nets, E.R. Caianiello, 7th Course on Ensemble Methods for Learning Machines, Vietri sul Mare, Italy (2002)Google Scholar
  19. 19.
    Fumera, G., Roli, F.: A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence (in press)Google Scholar
  20. 20.
    Wang, Z., Lu, L., Bovik, A.C.: Video Quality Assessment Based on Structural Distortion Measurement. Signal Processing: Image Communication 19, 121–132 (2004)CrossRefGoogle Scholar
  21. 21.
    Yager, R.R., Kacprzyk, J.: The Ordered Weighted Averaging Operation: Theory, Methodology and Applications. Kluwer, Norwell (1997)Google Scholar
  22. 22.
    Ceccarelli, M., Petrosino, A.: Multifeature adaptive classifiers for SAR image segmentation. Neurocomputing 14, 345–363 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Giuliano Laccetti
    • 1
  • Lucia Maddalena
    • 2
  • Alfredo Petrosino
    • 3
  1. 1.University of Naples,“Federico II”NaplesItaly
  2. 2.Italian National Research Council, ICARNaplesItaly
  3. 3.University of Naples “Parthenope”NaplesItaly

Personalised recommendations