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Image Restoration Based on Scene Adaptive Patch In-painting for Tampered Natural Scenes

  • Ravi Subban
  • Subramanyam Muthukumar
  • P. Pasupathi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 235)

Abstract

Many Researchers proposed algorithms which restored damaged images. These methods cause textures broken while inpainting texture image with complex structure. Most of the existing inpainting techniques require knowing beforehand where those damaged pixels are, either given as a priori or detected by some preprocessing. However, in certain applications, such information is neither available nor can be reliably pre-detected, like noise from archived photographs. This paper propose a patch based adaptive inpainting model to solve these types of problems, i.e., a model of simultaneously identifying and recovering damaged pixels of the given image. The proposed inpainting method is applied to various challenging image restoration tasks, including recovering images that are blurry and damaged by scratches. The experimental result shows that it is effective in inpainting complex texture images.

Keywords

Image Inpainting Image Decomposition Restoration Texture Segmentation Texture Synthesis Boundary Restoration Image Reconstruction Occlusion Removal 

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References

  1. 1.
    Mumford, Shah: Optimal approximations by piecewise smooth functions and associated Variational problems. Comm. Pure Appl. Math. 42(5), 577–685 (1989)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Bonet, J.S.D.: Multi resolution sampling procedure for analysis and synthesis of texture images. In: Computer Graphics. Annual conference Series, vol. 31, pp. 361–368 (1997)Google Scholar
  3. 3.
    Masnou, Morel: Level lines based disocclusion. In: Proc. IEEE-ICIP, pp. 259–263 (1998)Google Scholar
  4. 4.
    Efors, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: ICCV (2), pp. 1033–1038 (1999)Google Scholar
  5. 5.
    Bertalmio, et al.: Image in-painting. In: Siggraph, Computer Graphics Proceedings, pp. 417–424. ACM Press/ACM SIGGRAPH (2000)Google Scholar
  6. 6.
    Bertalmio, M.: Processing of flat and non-flat image information on arbitrary manifolds using partial Differential Equations. Computer Eng. Program (2001)Google Scholar
  7. 7.
    Chan, T.F., Shen, J.: Non Texture in-painting by curvature-driven diffusions (CDD). Journal of Vis. Comm. Image Rep. 4(12), 436–449 (2001)CrossRefGoogle Scholar
  8. 8.
    Meyer: Oscillating Patterns in Image Processing and Nonlinear Evolution Equations. University Lecture Series, vol. 22. AMS (2002)Google Scholar
  9. 9.
    Esedoglu, S., Shen, J.: Digital inpainting based on Mumford shaheuler image model. Eur. J. Appl. Math. 13, 353–370 (2002)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Drori, et al.: Fragment Based Image Completion. In: Proceedings of ACM SIGGRAPH (2003)Google Scholar
  11. 11.
    Criminisi, A., Perez, P., Toyama, K.: Region filling and object removal by exemplar based image in-painting. IEEE Trans. on Image Processing 13, 1200–1212 (2004)CrossRefGoogle Scholar
  12. 12.
    Aujol, et al.: Image decomposition into a bounded variation component and an oscillating component. J. MIV 22, 71–88 (2005)Google Scholar
  13. 13.
    Brennan: Simultaneous structure and texture image inpainting. Department of Computer Engineering, University of California at Santa Cruz, EE264 (2007)Google Scholar
  14. 14.
    Zhang, H.-B., Wang, J.-W.: Image In Painting by Integrating Structure and Texture Features. Journal of Beijing University of Technology 33(8), 864–869 (2007)Google Scholar
  15. 15.
    Xu, Z., et al.: Image Inpainting Algorithm Based on Partial Differential Equation. In: International Colloquium on CCCM (2008)Google Scholar
  16. 16.
    Muthukumar, S., et al.: Analysis of Image Inpainting Techniques with Exemplar, Poisson, Successive Elimination and 8 Pixel Neighborhood Methods. International Journal of Computer Applications 9(11), 15–18 (2010)CrossRefGoogle Scholar
  17. 17.
    Faizal, M., Fauzi, A., Lewis, P.H.: A multi-scale approach to texture-based image retrieval. Pattern Analysis and Applications 11, 141–157 (2007)Google Scholar
  18. 18.
    Xu, Z., Sun, J.: Image in-painting by patch propagation Using Patch Sparsity. IEEE Transactions on Image Processing 19(5) (2010)Google Scholar
  19. 19.
    Li, S., Zhao, M.: Image inpainting with salient structure completion and Texture propagation. Pattern Recognition, 0167-8655 (2011)Google Scholar
  20. 20.
    Du, X., et al.: Image segmentation and inpainting using hierarchical level set and texture mapping (2011)Google Scholar
  21. 21.
    Zhong, Z., Wang: Image inpainting-based edge enhancement using the eikonal equation (2011) 978-1-4577-0539-7/11 IEEEGoogle Scholar
  22. 22.
    Vidhya, B., Valarmathy, S.: Novel Video In-painting Using Patch Sparsity. In: IEEE – International Conference on Recent Trends in Information Technology, ICRTIT, 978-1-4577-0590- 8/11, IEEE, AnnaUniversity, Chennai (2011)Google Scholar
  23. 23.
    Ravi, S., et al.: Image Inpainting Techniques – A Survey And Analysis. In: International Conference on IIT, 978-1- 4673-6203-0/13© IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ravi Subban
    • 1
  • Subramanyam Muthukumar
    • 2
  • P. Pasupathi
    • 3
  1. 1.Dept. of Computer SciencePondicherry UniversityPondicherryIndia
  2. 2.Dept. of Computer Sci. and Engg.National Institute of TechnologyPuducherryIndia
  3. 3.Centre for Information Tech. and Engg.M.S. UniversityTirunelveliIndia

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