Skip to main content

Restoration of Archival Videos for Preserving Digital Heritage of India

  • Chapter
  • First Online:
Heritage Preservation

Abstract

Ever since the invention of motion pictures at the end of the nineteenth century, movies have played an important role in the cultural evolution of human society. Indian society is no exception. The large archives of monochrome as well as color movies, that are authentic evidences of many social, economic, and cultural changes that India has gone through, rightfully claim a place in our national heritage. Unfortunately, because of various factors namely, aging, improper preservation, inadequate imaging technology, many among these movies are severely degraded and show different visual artifacts. Each of the sources of degradation has a unique characteristic. Hence, it is not possible for a single method to restore all the artifacts faithfully. The degraded movies can be restored manually, but it is time consuming and expensive. we propose a unified approach to detect some of the most commonly appearing artifacts in heritage movies and restore them to achieve a superior visual quality.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://goo.gl/wcmE5Q.

  2. 2.

    https://goo.gl/zn4ClZ.

Abbreviations

ADM:

Alternating direction method

DCT:

Discrete cosine transform

LRSD:

Low rank-sparse decomposition technique

MM:

Majorization-minimization

PCA:

Partial color artifact

ROD:

Rank-order difference

SAD:

Sum of absolute distance

SALSA:

Split-augmented Lagrangian shrinkage algorithm

TVD:

Total variation-based decomposition

References

  1. Afonso, M.V., Bioucas-Dias, J.M., Figueiredo, M.A.: Fast image recovery using variable splitting and constrained optimization. IEEE Trans. Image Process. 19(9), 2345–2356 (2010)

    Article  MathSciNet  Google Scholar 

  2. Balster, E.J., Zheng, Y.F., Ewing, R.L.: Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising. IEEE Trans. Circuits Syst. Video Technol. 16(2), 220–230 (2006)

    Article  Google Scholar 

  3. Bertsekas, D.P., Tsitsiklis, J.N.: Parallel and Distributed Computation: Numerical Methods, vol. 23. Prentice Hall, Englewood Cliffs, NJ (1989)

    MATH  Google Scholar 

  4. Bhattacharya, S., Venkatsh, K., Gupta, S.: Background estimation and motion saliency detection using total variation-based video decomposition. Signal, Image Video Process. 11(1), 113–121 (2017)

    Article  Google Scholar 

  5. Bouguet, J.Y.: Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corp. 5(1–10), 4 (2001)

    Google Scholar 

  6. Buades, A., Coll, B., Morel, J.M.: Denoising image sequences does not require motion estimation. In: IEEE Conference on Advanced Video and Signal Based Surveillance, 2005. AVSS 2005, pp. 70–74. IEEE (2005)

    Google Scholar 

  7. Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)

    Article  MathSciNet  Google Scholar 

  8. Candès, E.J., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717–772 (2009)

    Article  MathSciNet  Google Scholar 

  9. Chambolle, A.: Total variation minimization and a class of binary MRF models. In: Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Sciences, vol. 3757, pp. 136–152. Springer (2005)

    Google Scholar 

  10. Chan, T.W., Au, O.C., Chong, T.S., Chau, W.S.: A novel content-adaptive video denoising filter. In: Proceedings.(ICASSP’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, vol. 2, pp. ii–649. IEEE (2005)

    Google Scholar 

  11. Chen, G., Zhang, J., Li, D., Chen, H.: Robust kronecker product video denoising based on fractional-order total variation model. Signal Process. 119, 1–20 (2016)

    Article  Google Scholar 

  12. Crawford, A.J., Bruni, V., Kokaram, A.C., Vitulano, D.: Multi-scale semi-transparent blotch removal on archived photographs using Bayesian matting techniques and visibility laws. In: 2007 IEEE International Conference on Image Processing, vol. 1, pp. I–561. IEEE (2007)

    Google Scholar 

  13. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  14. Delon, J.: Movie and video scale-time equalization application to flicker reduction. IEEE Trans. Image Process. 15(1), 241–248 (2006)

    Article  Google Scholar 

  15. Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)

    Article  MathSciNet  Google Scholar 

  16. Figueiredo, M.A., Bioucas-Dias, J.M., Nowak, R.D.: Majorization-minimization algorithms for wavelet-based image restoration. IEEE Trans. Image Process. 16(12), 2980–2991 (2007)

    Article  MathSciNet  Google Scholar 

  17. Hoshi, T., Komatsu, T., Saito, T.: Film blotch removal with a spatiotemporal fuzzy filter based on local image analysis of anisotropic continuity. In: 1998 International Conference on Image Processing, 1998. ICIP 98. Proceedings, vol. 2, pp. 478–482. IEEE (1998)

    Google Scholar 

  18. Hwang, H., Haddad, R.A.: Adaptive median filters: new algorithms and results. IEEE Trans. Image Process. 4(4), 499–502 (1995)

    Article  Google Scholar 

  19. Ji, H., Huang, S., Shen, Z., Xu, Y.: Robust video restoration by joint sparse and low rank matrix approximation. SIAM J. Imaging Sci. 4(4), 1122–1142 (2011)

    Article  MathSciNet  Google Scholar 

  20. Ji, H., Liu, C., Shen, Z., Xu, Y.: Robust video denoising using low rank matrix completion. In: CVPR, pp. 1791–1798. Citeseer (2010)

    Google Scholar 

  21. Jin, F., Fieguth, P., Winger, L.: Wavelet video denoising with regularized multiresolution motion estimation. EURASIP J. Appl. Signal Process. 2006, 109–109 (2006)

    MATH  Google Scholar 

  22. Kokaram, A.C.: 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 (2004)

    Article  Google Scholar 

  23. Kokaram, A.C.: 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 (2004)

    Article  Google Scholar 

  24. Li, H., Lu, Z., Wang, Z., Ling, Q., Li, W.: Detection of blotch and scratch in video based on video decomposition. IEEE Trans. Circuits Syst. Video Technol. 23(11), 1887–1900 (2013)

    Article  Google Scholar 

  25. Lin, Z., Ganesh, A., Wright, J., Wu, L., Chen, M., Ma, Y.: Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix. In: Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), vol. 61 (2009)

    Google Scholar 

  26. Liu, Y.L., Wang, J., Chen, X., Guo, Y.W., Peng, Q.S.: A robust and fast non-local means algorithm for image denoising. J. Comput. Sci. Technol. 23(2), 270–279 (2008)

    Article  MathSciNet  Google Scholar 

  27. Lu, Q., Lu, Z., Tao, X., Li, H.: A new non-local video denoising scheme using low-rank representation and total variation regularization. In: 2014 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2724–2727. IEEE (2014)

    Google Scholar 

  28. Maggioni, M., Boracchi, G., Foi, A., Egiazarian, K.: Video denoising, deblocking, and enhancement through separable 4-D nonlocal spatiotemporal transforms. IEEE Trans. Image Process. 21(9), 3952–3966 (2012)

    Article  MathSciNet  Google Scholar 

  29. Mansour, H., Saab, R., Nasiopoulos, P., Ward, R.: Color image desaturation using sparse reconstruction. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 778–781. IEEE (2010)

    Google Scholar 

  30. Narendra, V., Gupta, S.: Restoration of partial color artifact and blotches using histogram matching and sparse technique. In: 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4. IEEE (2013)

    Google Scholar 

  31. Newson, A., Almansa, A., Fradet, M., Gousseau, Y., Pérez, P.: Video inpainting of complex scenes. arXiv:1503.05528 (2015)

  32. Rahman, S.M., Ahmad, M.O., Swamy, M.: Video denoising based on inter-frame statistical modeling of wavelet coefficients. IEEE Trans. Circuits Syst. Video Technol. 17(2), 187–198 (2007)

    Article  Google Scholar 

  33. Rares, A., Reinders, M.J.T., Biemond, J.: Restoration of films affected by partial color artefacts. In: Proceedings of EUSIPCO, vol. 1, pp. 609–612 (2002)

    Google Scholar 

  34. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60(1), 259–268 (1992)

    Article  MathSciNet  Google Scholar 

  35. Saito, T., Komatsu, T., Ohuchi, T., Hoshi, T.: Practical nonlinear filtering for removal of blotches from old film. In: 1999 International Conference on Image Processing, 1999. ICIP 99. Proceedings, vol. 3, pp. 164–168. IEEE (1999)

    Google Scholar 

  36. Selesnick, I.W., Bayram, I.: Total variation filtering. http://citeseerx.ist.psu.edu/viewdoc/download (2009)

  37. Tenze, L., Ramponi, G., Carrato, S.: Blotches correction and contrast enhancement for old film pictures. In: 2000 International Conference on Image Processing, 2000. Proceedings, vol. 2, pp. 660–663. IEEE (2000)

    Google Scholar 

  38. Vlachos, T.: Flicker correction for archived film sequences using a nonlinear model. IEEE Trans. Circuits Syst. Video Technol. 14(4), 508–516 (2004)

    Article  Google Scholar 

  39. Wang, Z., Li, H., Ling, Q., Li, W.: Robust temporal-spatial decomposition and its applications in video processing. IEEE Trans. Circuits Syst. Video Technol. 23(3), 387–400 (2013)

    Article  Google Scholar 

  40. Wong, K., Das, A., Chong, M.: Improved flicker removal through motion vectors compensation. In: Third International Conference on Image and Graphics (ICIG’04), pp. 552–555. IEEE (2004)

    Google Scholar 

  41. Yuan, X., Yang, J.: Sparse and low rank matrix decomposition via alternating direction method. http://www.optimization-online.org/DB_FILE/2009/11/2447.pdf (2009)

  42. Zhou, T., Tao, D.: GoDec: randomized low-rank & sparse matrix decomposition in noisy case. In: International Conference on Machine Learning (2011)

    Google Scholar 

  43. Zlokolica, V., Pižurica, A., Philips, W.: Wavelet-domain video denoising based on reliability measures. IEEE Trans. Circuits Syst. Video Technol. 16(8), 993–1007 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Saumik Bhattacharya .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bhattacharya, S., Venkatesh, K.S., Gupta, S. (2018). Restoration of Archival Videos for Preserving Digital Heritage of India. In: Chanda, B., Chaudhuri, S., Chaudhury, S. (eds) Heritage Preservation. Springer, Singapore. https://doi.org/10.1007/978-981-10-7221-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7221-5_10

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7220-8

  • Online ISBN: 978-981-10-7221-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics