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Video Inpainting Based on Re-weighted Tensor Decomposition

  • Anjali Ravindran
  • M. Baburaj
  • Sudhish N. George
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 703)

Abstract

Video inpainting is the process of improving the information content in a video by removing irrelevant video objects and restoring lost or deteriorated parts utilizing the spatiotemporal features that are available from adjacent frames. This paper proposes an effective video inpainting technique utilizing the multi-dimensional data decomposition technique. In Tensor Robust Principal Component Analysis (TRPCA), a multi-dimensional data corrupted by gross errors is decomposed into a low multi-rank component and a sparse component. The proposed method employs an improved version of TRPCA called Re-weighted low-rank Tensor Decomposition (RWTD) to separate the true information and the irrelevant sparse components in a video. Through this, manual identification of the components which have to be removed is avoided. Subsequent inpainting algorithm fills the region with appropriate and visually plausible data. The capabilities of the proposed method are validated by applying into videos having moving sparse outliers in it. The experimental results reveal that the proposed method performs well compared with other techniques.

Keywords

Video inpainting Tensor decomposition Sparsity Low-rank tensor recovery 

References

  1. 1.
    S. Moran, “Video inpainting,” vol. 1, pp. 12–25, 2009.Google Scholar
  2. 2.
    W. Zhang, S. Cheung, and M. Chen, “Hiding privacy information in video surveillance system,” in Image Processing, 2005. ICIP 2005. IEEE International Conference on, vol. 3. IEEE, 2005, pp. II–868.Google Scholar
  3. 3.
    Y. Umeda and K. Arakawa, “Removal of film scratches using exemplar-based inpainting with directional median filter,” in Communications and Information Technologies (ISCIT), 2012 International Symposium on. IEEE, 2012, pp. 6–11.Google Scholar
  4. 4.
    S. Yoo and R.-H. Park, “Red-eye detection and correction using inpainting in digital photographs,” IEEE Transactions on Consumer Electronics, vol. 55, no. 3, pp. 1006–1014, 2009.CrossRefGoogle Scholar
  5. 5.
    V. V. Mahalingam, Digital inpainting algorithms and evaluation. University of Kentucky, 2010.Google Scholar
  6. 6.
    M. Bertalmio, A. L. Bertozzi, and G. Sapiro, “Navier-stokes, fluid dynamics, and image and video inpainting,” in Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1. IEEE, 2001, pp. I–I.Google Scholar
  7. 7.
    W.-Q. Yan and M. S. Kankanhalli, “Erasing video logos based on image inpainting,” in Multimedia and Expo, 2002. ICME’02. Proceedings. 2002 IEEE International Conference on, vol. 2. IEEE, 2002, pp. 521–524.Google Scholar
  8. 8.
    A. Newson, A. Almansa, M. Fradet, Y. Gousseau, and P. Pérez, “Video inpainting of complex scenes,” SIAM Journal on Imaging Sciences, vol. 7, no. 4, pp. 1993–2019, 2014.MathSciNetCrossRefGoogle Scholar
  9. 9.
    T. K. Shih, N. C. Tang, and J.-N. Hwang, “Exemplar-based video inpainting without ghost shadow artifacts by maintaining temporal continuity,” IEEE transactions on circuits and systems for video technology, vol. 19, no. 3, pp. 347–360, 2009.CrossRefGoogle Scholar
  10. 10.
    M. V. Venkatesh, S.-c. S. Cheung, and J. Zhao, “Efficient object-based video inpainting,” Pattern Recognition Letters, vol. 30, no. 2, pp. 168–179, 2009.CrossRefGoogle Scholar
  11. 11.
    P. Kumar and P. Puttaswamy, “Moving text line detection and extraction in tv video frames,” in Advance Computing Conference (IACC), 2015 IEEE International. IEEE, 2015, pp. 24–28.Google Scholar
  12. 12.
    C. Lu, J. Feng, Y. Chen, W. Liu, Z. Lin, and S. Yan, “Tensor robust principal component analysis: Exact recovery of corrupted low-rank tensors via convex optimization,” in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, (CVPR), 2016.Google Scholar
  13. 13.
    M. E. Kilmer, K. Braman, N. Hao, and R. C. Hoover, “Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging,” SIAM Journal on Matrix Analysis and Applications, vol. 34, no. 1, pp. 148–172, 2013.MathSciNetCrossRefGoogle Scholar
  14. 14.
    B. M. and S. N. George, “Reweighted low-rank tensor decomposition and its applications in video denoising,” CoRR, vol. abs/1611.05963, 2016. [Online]. Available.Google Scholar
  15. 15.
    D. Goldfarb and Z. Qin, “Robust low-rank tensor recovery: Models and algorithms,” SIAM Journal on Matrix Analysis and Applications, vol. 35, no. 1, pp. 225–253, 2014.MathSciNetCrossRefGoogle Scholar
  16. 16.
    C. D. Martin, R. Shafer, and B. LaRue, “An order-p tensor factorization with applications in imaging,” SIAM Journal on Scientific Computing, vol. 35, no. 1, pp. A474–A490, 2013. [Online]. Available.MathSciNetCrossRefGoogle Scholar
  17. 17.
    T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM Review, vol. 51, no. 3, pp. 455–500, 2009. [Online]. Available.MathSciNetCrossRefGoogle Scholar
  18. 18.
    V. Chandrasekaran, S. Sanghavi, P. A. Parrilo, and A. S. Willsky, “Rank-sparsity incoherence for matrix decomposition,” SIAM Journal on Optimization, vol. 21, no. 2, pp. 572–596, 2011.MathSciNetCrossRefGoogle Scholar
  19. 19.
    M. Yan and W. Yin, “Self equivalence of the alternating direction method of multipliers,” arXiv preprint arXiv:1407.7400, 2014.
  20. 20.
    X. Yuan, “Alternating direction methods for sparse covariance selection,” preprint, 2009.Google Scholar
  21. 21.
    Z. Lin, R. Liu, and Z. Su, “Linearized alternating direction method with adaptive penalty for low-rank representation,” in Advances in neural information processing systems, 2011, pp. 612–620.Google Scholar
  22. 22.
    Y. Xu and W. Yin, “A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion,” SIAM Journal on imaging sciences, vol. 6, no. 3, pp. 1758–1789, 2013.MathSciNetCrossRefGoogle Scholar
  23. 23.
  24. 24.
    A. Sobral, T. Bouwmans, and E.-h. Zahzah, “Lrslibrary: Low-rank and sparse tools for background modeling and subtraction in videos,” in Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing. CRC Press, Taylor and Francis Group.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Anjali Ravindran
    • 1
  • M. Baburaj
    • 2
    • 3
  • Sudhish N. George
    • 4
  1. 1.Department of Electronics and Communication EngineeringGovernment College of Engineering KannurKannurIndia
  2. 2.Department of Electronics and Communication EngineeringNational Institute of Technology CalicutCalicutIndia
  3. 3.Government College of Engineering KannurKannurIndia
  4. 4.Department of Electronics and Communication EngineeringNational Institute of Technology CalicutCalicutIndia

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