CCPR 2016: Pattern Recognition pp 245-258 | Cite as

Image Inpainting Based on Sparse Representation with Dictionary Pre-clustering

Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 663)

Abstract

This paper proposed a new image inpainting algorithm based on sparse representation. In traditional exemplar-based methods, the image patch is inpainted by the best matched patch from the source region. This greedy search will introduce unwanted objects and has huge time consuming. The proposed algorithm directly employs all the known image patches to form an over-complete dictionary. And then, the over-complete dictionary is clustered into several sub-dictionaries. Finally, the unrepaired image patches are repaired over their corresponding closest sub-dictionaries through non-negative orthogonal matching pursuit algorithm. Experimental results show that the proposed method achieves superior performance than state-of-the-art methods. In addition, the time complexity is greatly reduced in comparison with the traditional exemplar-based inpainting algorithm.

Keywords

Image inpainting Sparse representation Over-complete dictionary 
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Notes

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 61432014 and Grant 61501339, in part by the Fundamental Research Funds for the Central Universities under Grant BDZ021403, Grant XJS15049, Grant XJS15068, and Grant JB160104, and in part by the China Post-Doctoral Science Foundation under Grant 2015M580818, and Grant 2016T90893.

References

  1. 1.
    Bertalmio, M., Sapiro, G., Ballester, C., Caselles, V.: Image inpainting. In: Computer Graphics and Interactive Techniques, pp. 417–424 (2000)Google Scholar
  2. 2.
    Bertalmio, M., Bertozzi, A.L., Sapiro, G.: Navier-stokes, fluid dynamics, and image and video inpainting. In: Computer Vision and Pattern Recognition, pp. 355–362 (2001)Google Scholar
  3. 3.
    Telea, A.: An image inpainting technique based on the fast marching method. J. Graph. Tool 9(1), 23–24 (2004)CrossRefGoogle Scholar
  4. 4.
    Tschumperl, D.: Fast anisotropic smoothing of multi-valued images using curvature-preserving PDE’s. Int. J. Comput. Vision 68(1), 65–82 (2006)CrossRefGoogle Scholar
  5. 5.
    Chan, T., Shen, J.: Local inpainting models and tv inpainting. SIAM J. Appl. Math. 62(3), 1019–1043 (2001)MathSciNetGoogle Scholar
  6. 6.
    Efros, A., Leung, T.: Texture synthesis by non-parametric sampling. In: The Proceedings of IEEE International Conference on Computer Vision, pp. 1033–1038 (1999)Google Scholar
  7. 7.
    Bertalmio, G.S.M., Vese, L., Osher, S.: Simultaneous structure and texture image inpainting. IEEE Trans. Image Process. 12(8), 882–889 (2003)CrossRefGoogle Scholar
  8. 8.
    Criminisi, P.P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 13(9), 1200–1212 (2004)CrossRefGoogle Scholar
  9. 9.
    Wong, A., Orchard, J.: A nonlocal-means approach to exemplar-based inpainting. In: Proceedings of IEEE International Conference on Image Processing, pp. 2600–2603 (2008)Google Scholar
  10. 10.
    Shen, B., Hu, W., Zhang, Y., Zhang, Y.J.: Image inpainting via sparse representation. In: Proceedings of IEEE Conference on Acoustics Speech and Signal Processing, pp. 697–700 (2009)Google Scholar
  11. 11.
    Xu, Z., Sun, J.: Image inpainting by patch propagation using patch sparsity. IEEE Trans. Image Process. 19(5), 1153–1165 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ogawa, T., Haseyama, M.: Image inpainting based on sparse representations with a perceptual metric. EURASIP J. Adv. Signal Process. 2013(1), 1–26 (2013)CrossRefGoogle Scholar
  13. 13.
    Davis, G., Mallat, S., Avellaneda, M.: Adaptive greedy approximations. Constr. Approximation 13(1), 57–98 (1997)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Candés, E.J.: Compressive sampling. In: Proceedings of the International Congress of Mathematicians, pp. 1433–1452 (2006)Google Scholar
  15. 15.
    Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decompositionby basis pursuit. SIAM Rev. 43(1), 129–159 (2001)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Lu, W., Vaswani, N.: Modified basis pursuit denoising (MODIFIED-BPDN) for noisy compressive sensing with partially known support. In: Proceedings of IEEE Conference on Acoustics Speech and Signal Processing, pp. 3926–3929 (2010)Google Scholar
  17. 17.
    Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Advances in Neural Information Processing Systems, pp. 801–808 (2006)Google Scholar
  18. 18.
    Mallat, S.G., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993)CrossRefMATHGoogle Scholar
  19. 19.
    Chen, L.W., Chen, S., Billings, S.A., Luo, W.: Orthogonal least squares methods and their application to non-linear system identification. Int. J. Control 50(5), 1873–1896 (1989)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Yaghoobi, M., Wu, D., Davies, M.E.: Fast non-negative orthogonal matching pursuit. Signal Process. Lett. 22(9), 1229–1233 (2015)CrossRefGoogle Scholar
  21. 21.
    Cai, D.: Litekmeans: the fastest matlab implementation of kmeans (2011). http://www.zjucadcg.cn/dengcai/Data/Clustering.html

Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Integrated Services Networks, School of Electronic EngineeringXidian UniversityXi’anChina
  2. 2.State Key Laboratory of Integrated Services Networks, School of Telecommunications EngineeringXidian UniversityXi’anChina

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