Skip to main content
SpringerLink
Log in

Features Based Spatial and Temporal Blotch Detection for Archive Video Restoration

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

In order to restore blotched archive video without causing distortion to areas of the frames that are not corrupted, the locations of the blotches must be identified. Blotch detection usually needs reliable motion estimation to avoid false detection of uncorrupted regions in existing techniques. In this paper a blotch artifacts detection technique, which is based on spatiotemporal blotch image features extraction to avoid the dependence on the motion estimation, is proposed. In order to greatly reduce false alarms due to motion estimation errors the proposed detection technique is first to detect blotch candidates based on their spatial features and then to detect blotches from the blotch candidates by their temporal intensity discontinuities. Experimental results show that the proposed technique significantly improves detection performance and outperforms existing techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

References

  1. Kokaram, A. C. (1998). Motion picture restoration, digital algorithms for artefact suppression in degraded motion picture film and video. Heidelberg: Springer.

    Google Scholar 

  2. Nadenau, M. J., & Mitra, S. K. (1997). Blotch and scratch detection in image sequences based on rank ordered differences. In Time-varying image processing and moving object recognition (pp. 27–35). Amsterdam: Elsevier.

  3. Lagendijk, R. L., Van Roosmalen, P. M. B., Biemond, J. (2000). Video enhancement and restoration. Handbook of image & video processing (pp. 227–242). San Diego: Academic Press.

  4. Kokaram, A. C., Morris, R., Fitzgerald, W., & Rayner, P. (1995). Detection of missing data in image sequences. IEEE Transactions on Image Processing, 4(11), 1496–1508.

    Article  Google Scholar 

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

  6. Hamid, M. S., Harvey, N. R., & Marshall, S. (2003). Genetic algorithm optimization of multidimensional grayscale soft morphological filters with applications in film archive restoration. IEEE Transactions on Circuits and Systems for Video Technology, 13(5), 406–416.

    Article  Google Scholar 

  7. Ren, J., & Vlachos, T. (2007). Missing-data recovery from dirt sparkles on degraded color films. Optical Engineering, 46(7), 077001.

    Article  Google Scholar 

  8. Ren, J., & Vlachos, T. (2007). Detection and recovery of film dirt for archive restoration applications. IEEE International Conference on Image Processing, 4, 21–24.

    Google Scholar 

  9. Ren, J, & Vlachos, T. (2005). “Dirt detection for archive film restoration using an adaptive spatio-temporal approach.” In Proc. the 2nd IEE European Conf. Visual Media Production (pp. 221–230).

  10. Buades, A., Delon, J., Gousseau, Y., Masnou, S. (2010). “Adaptive blotches detection for film restoration.” In Proceedings of 2010 I.E. 17th International Conference on Image Processing. Hong Kong.

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

    Article  Google Scholar 

  12. Van Roosmalen, P. M. B. (1999). Restoration of archived film and video, Ph.D. dissertation, Tech. Univ. Delft, Delft, The Netherlands, Sept.

  13. Ren, J., & Vlachos, T. (2010). Detection of dirt impairment from archived film sequences: survey and evaluation. Optical Engineering, 49(6), 067005–067012.

    Article  Google Scholar 

  14. Gangal, A., Kayikçioglu, T., Dizdaroglu, B. (2004). “An improved motion compensated restoration method for damage color motion picture film.” Signal Processing: Image Communication (19), 353–368.

  15. Buisson, O., Boukir, S., Besserer, B. (2003). “Motion compensated film restoration”. Machine vision and applications, vol. 13, no. 4 (pp. 201–212). Heidelberg, Springer.

  16. Bovik, A. (2006). Handbook of image and video processing, 2nd ed. Elsevier.

  17. Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H. (2009). “Anisotropic Huber-L1 optical flow.” Proceedings of the British Machine Vision Conference, London, UK.

  18. Wedel, A., Pock, T., Braun, J., Franke, U., Cremers, D. (2008). “Duality TV-L1 flow with fundamental matrix prior.” In Proceedings of Image and Vision Computing. New Zealand.

  19. Czúni, L., Hanis, A., Kovács, L., Kránicz, B., Licsár, A., Szirányi, T., Kas, I., Kovács, G., & Manno, S. (2004). Digital motion picture restoration system for film archives (DIMORF). SMPTE Motion Imaging Journal, 113, 170–176.

    Article  Google Scholar 

  20. Delaney, B., & Hoomans, B. (2004). Prestospace user requirements feedback meeting in London. In An integrated solution for audio- visual preservation and access.

  21. Ren, J., & Vlachos, T. (2007). Segmentation-assisted detection of dirt impairments in archived film sequences. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 37(2), 463–470.

    Article  Google Scholar 

  22. Licsár, A., Szirányi, T., Czúni, L. (2007). “Trainable blotch detection on high resolution archive films minimizing the human interaction.” Machine Vision and Applications. Springer.

  23. Corrigan, D., Harte, N., Kokaram, A. (2008). “Pathological motion detection fro robust missing data treatment.” EURASIP. J. Adv. Signal Processing (pp. 1–16).

  24. Tilie, S., Bloch, I., & Laborelli, L. (2007). Fusion of complementary detectors for improving blotch detection in digitised films. Pattern Recognition Letters, 23(16), 1735–1746.

    Article  Google Scholar 

  25. Xu, Z., Wu, H. R., Qiu, B., & Yu, X. (2009). Geometric features based filtering for suppression of impulse noise in color images. IEEE Transactions on Image Processing, 18(8), 1742–1759.

    Article  MathSciNet  Google Scholar 

  26. van Roosmalen, P. M. B., Lagendijk, R. L., & Biemond, J. (1999). Correction of intensity flicker in old film sequences. IEEE Transactions on Circuits and Systems for Video Technology, 9(7), 1013.

    Article  Google Scholar 

  27. Licsár A., Czúni L., Szirányi T. (2003). “Adaptive stabilization of vibration on archive films.” Lecture Notes in C.S, LNCS 2756 (pp. 230–237). Heidelberg: Springer.

  28. Hill, L., & Vlachos, T. (1999) “On the estimation of global motion using phase correlation for broadcast applications.” In Proceedings of the IEEE int. Conf on Image Processing and it’s Applications (pp. 721–725). Manchester.

  29. Ren, J., & Vlachos, T. (2007). Efficient detection of temporally impulsive dirt impairments in archived films. Signal Processing, 87(3), 541–551.

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Chan, R. H., Ho, C.-W., & Nikolova, M. (2005). Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans. on Image Processing, 14(10), 1479–1485.

    Article  Google Scholar 

  32. Lukac, R., Smolka, B., Martin, K., Plataniotis, K. N., & Venetsanopoulos, A. N. (2005). Vector filtering for color imaging. IEEE Signal Processing Magazine, Special Issue on Color Image Processing, 22(1), 74–86.

    Article  Google Scholar 

  33. Sonka, M., Hlavac, V, Boyle, R. (2008). Image processing, analysis, and machine vision. Thomson.

  34. Schallauer, P., Pinz, A., Haas, W. “Automatic restoration algorithms for 35 mm film.” Videre Journal of Computer Vision Research, 1(3), 60–85.

  35. Baase, S., & Van Gelder, A. (2001). Computer algorithms: Introduction to design and analysis, 3rd ed. Pearson Education.

  36. VQEG test sequences: http://www.its.bldrdoc.gov/vqeg.

Download references

Acknowledgments

This research was supported under Australian Research Council’s Discovery Projects funding scheme (project number 0988654).

Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, RMIT University, Melbourne, Vic, 3001, Australia

    Zhengya Xu, Hong Ren Wu & Xinghuo Yu

  2. Faculty of Information Technology, Monash University, Clayton, VC, 3800, Australia

    Bin Qiu

Authors
  1. Zhengya Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Ren Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinghuo Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhengya Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, Z., Wu, H.R., Yu, X. et al. Features Based Spatial and Temporal Blotch Detection for Archive Video Restoration. J Sign Process Syst 81, 213–226 (2015). https://doi.org/10.1007/s11265-014-0942-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-014-0942-8

Keywords

Search

Navigation