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Trainable blotch detection on high resolution archive films minimizing the human interaction

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Abstract

Film archives are continuously in need of automatic restoration tools to accelerate the correction of film artifacts and to decrease the costs. Blotches are a common type of film degradation and their correction needs a lot of manual interaction in traditional systems due to high false detection rates and the huge amount of data of high resolution images. Blotch detectors need reliable motion estimation to avoid the false detection of uncorrupted regions. In case of erroneous detection, usually an operator has to remove the false alarms manually, which significantly decreases the efficiency of the restoration process. To reduce manual intervention, we developed a two-step false alarm reduction technique including pixel- and object-based methods as post-processing. The proposed pixel-based algorithm compensates motion, decreasing false alarms at low computational cost, while the following object based method further reduces the residual false alarms by machine learning techniques. We introduced a new quality metric for detection methods by measuring the required amount of manual work after the automatic detection. In our novel evaluation technique, the ground truth is collected from digitized archive sequences where defective pixel positions are detected in an interactive process.

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References

  1. Delaney, B., Hoomans, B.: Prestospace user requirements feedback meeting in London. In: An Integrated Solution for Audio-Visual Preservation and Access (2004)

  2. Czúni L., Hanis A., Kovács L., Kránicz B., Licsár A., Szirányi T., Kas I., Kovács Gy. and Manno S. (2004). Digital motion picture restoration system for film archives (DIMORF). SMPTE Motion Imaging J. 113: 170–176

    Google Scholar 

  3. Kokaram A.C. (2004). On missing data treatment for degraded video and film archives: a survey and a new Bayesian approach. IEEE Trans. Image Process. 13(3): 397–415

    Article  Google Scholar 

  4. Ghaderi, M., Kasaei, S.: Novel post-processing methods used in detection of blotches in image sequences. AEÜ Int. J. Electron. Commun. (58), 58–64 (2004)

  5. Kokaram A.C., Morris R., Fitzgerald W. and Rayner P. (1995). Detection of missing data in image sequences. IEEE Trans. Image Process. 4(11): 1496–1508

    Article  Google Scholar 

  6. Gangal, A., Kayikçioglu, T., Dizdaroglu, B.: An improved motion-compensated restoration method for damage color motion picture film. Signal Proc. Image Commun. (19), 353–368 (2004)

  7. Nadenau, M.J., Mitra, S.K.: Blotch and scratch detection in image sequences based on rank ordered differences. In: Time-Varying Image Processing and Moving Object Recognition, pp. 27–35. Elsevier, Amsterdam (1997)

  8. Tilie, S., Laborelli, L.: Blotch detection for digital archives restoration based on the fusion of spatial and temporal detectors. In: The 9th International Conference on Information Fusion, Florence, Italy, pp. 1–8 (2006)

  9. Ren, J., Vlachos, T.: Segmentation-assisted dirt detection for the restoration of archived films. Proceedings of BMVC, vol. 1, Oxford, England, pp. 359–368 (2005)

  10. Licsár, A., Czúni, L., Szirányi, T.: Trainable post-processing method to reduce false alarms in the detection of small blotches of archive films. IEEE International Conference on Image Processing (ICIP), Italy, pp. 562–565 (2005)

  11. Naranjo, V., Albiol, A., Mossi, J.M., Albiol, A.: Morphological Lambda Reconstruction Applied to Restoration of Blotches in Old Films. Proceedings of the 4th IASTED International Conference on Visualisation, Imaging and Image Processing, Spain (2004)

  12. Tenze, L., Ramponi, G., Carrato, S.: Robust detection and correction of blotches in old films using spatio-temporal information. Proceedings of SPIE International Symp. Electronic, San Jose, pp. 348–357 (2002)

  13. Joyeux L., Boukir S., Besserer B. and Buisson O. (2001). Reconstruction of degraded image sequences. Application to film restoration. Image and Vision Computing (MAVIS) 19(8): 503–516

    Google Scholar 

  14. Tenze, L., Ramponi, G., Carrato, S.: Blotches Correction and Contrast Enhancement for Old Film Pictures. Proceedings of the International Conference on Image Processing, ICIP, Canada, pp. 660–663 (2000)

  15. Kokaram A.C. (1998). Motion Picture Restoration: Digital Algorithms for Artefact Suppression in Degraded Motion Picture Film and Video. Springer, Heidelberg

    Google Scholar 

  16. Lagendijk, R.L., van Roosmalen, P.M.B., Biemond, J.: Video enhancement and restoration. Handbook of Image& Video Processing, Academic Press, San Diego, pp. 227–242 (2000)

  17. Hamid M.S., Harvey N.R. and Marshall S. (2003). Genetic algorithm optimization of multidimensional grayscale soft morphological filters with applications in film archive restoration. IEEE Trans. Circuits Syst. Video Technol. 13(5): 406–416

    Article  Google Scholar 

  18. Buisson, O., Boukir, S., Besserer, B.: Motion Compensated Film Restoration. Machine Vision and Applications, Vol 13, Issue 4, pp. 201–212 Springer, Heidelberg (2003)

  19. Schallauer P., Pinz A. and Haas W. (1999). Automatic restoration algorithms for 35 mm film. Videre J. Comput. Vis. Res. 1(3): 60–85

    Google Scholar 

  20. Licsár, A., Czúni, L., Szirányi, T.: Adaptive Stabilization of Vibration on Archive Films. Lecture Notes in C.S, LNCS 2756 pp. 230–237. Springer, Heidelberg (2003)

  21. Bergen J.R., Burt P.J., Hingorani R. and Peleg S. (1992). A three-frame algorithm for estimating two-component image motion. IEEE Trans. Pattern Anal. Mach. Intell. 14(9): 886–896

    Article  Google Scholar 

  22. Burt P.J. and Adelson E.H. (1983). The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4): 532–540

    Article  Google Scholar 

  23. Fukushima K. (1975). Cognitron: a self-organizing multilayered neural network. Biol. Cybern. 20: 121–136

    Article  Google Scholar 

  24. Cortes C. and Vapnik V. (1995). Support-vector network. Mach. Learn. 20: 273–297

    MATH  Google Scholar 

  25. Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by error propagation. Parallel Distributed Processing, MIT Press, Cambridge, pp. 318–362 (1986)

  26. Czúni, L., Császár, G., Hanis, A., Kovács, L., Licsár, A., Szirányi, T.: Semi automatic digital motion picture restoration system with learning capabilities. In: Learning for Adaptable Visual Systems (LAVS), Cambridge, UK, 2004

  27. Cybenko G. (1989). Approximation by Superposition of a Sigmoidal Function. Math. Control Signals Syst. 2: 303–314

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Image Processing and Neurocomputing, University of Pannonia, Egyetem str. 10, 8200, Veszprém, Hungary

    Attila Licsár & László Czúni

  2. Distributed Events Analysis Research Group, Computer and Automation Research Institute, Hungarian Academy of Sciences, Kende u. 13-17, 1111, Budapest, Hungary

    Tamás Szirányi

Authors
  1. Attila Licsár
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  2. Tamás Szirányi
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  3. László Czúni
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Correspondence to Attila Licsár.

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Licsár, A., Szirányi, T. & Czúni, L. Trainable blotch detection on high resolution archive films minimizing the human interaction. Machine Vision and Applications 21, 767–777 (2010). https://doi.org/10.1007/s00138-007-0106-y

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  • DOI: https://doi.org/10.1007/s00138-007-0106-y

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