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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
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
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)
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)
Bertsekas, D.P., Tsitsiklis, J.N.: Parallel and Distributed Computation: Numerical Methods, vol. 23. Prentice Hall, Englewood Cliffs, NJ (1989)
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)
Bouguet, J.Y.: Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corp. 5(1–10), 4 (2001)
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)
Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)
Candès, E.J., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717–772 (2009)
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)
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)
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)
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)
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)
Delon, J.: Movie and video scale-time equalization application to flicker reduction. IEEE Trans. Image Process. 15(1), 241–248 (2006)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
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)
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)
Hwang, H., Haddad, R.A.: Adaptive median filters: new algorithms and results. IEEE Trans. Image Process. 4(4), 499–502 (1995)
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)
Ji, H., Liu, C., Shen, Z., Xu, Y.: Robust video denoising using low rank matrix completion. In: CVPR, pp. 1791–1798. Citeseer (2010)
Jin, F., Fieguth, P., Winger, L.: Wavelet video denoising with regularized multiresolution motion estimation. EURASIP J. Appl. Signal Process. 2006, 109–109 (2006)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Newson, A., Almansa, A., Fradet, M., Gousseau, Y., Pérez, P.: Video inpainting of complex scenes. arXiv:1503.05528 (2015)
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)
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)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60(1), 259–268 (1992)
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)
Selesnick, I.W., Bayram, I.: Total variation filtering. http://citeseerx.ist.psu.edu/viewdoc/download (2009)
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)
Vlachos, T.: Flicker correction for archived film sequences using a nonlinear model. IEEE Trans. Circuits Syst. Video Technol. 14(4), 508–516 (2004)
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)
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)
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)
Zhou, T., Tao, D.: GoDec: randomized low-rank & sparse matrix decomposition in noisy case. In: International Conference on Machine Learning (2011)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
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)