Cluster Computing

, Volume 22, Supplement 3, pp 7593–7601 | Cite as

The research of image inpainting algorithm using self-adaptive group structure and sparse representation

  • Jiangchun MoEmail author
  • Yucai Zhou


Focused on the issue that the object structure discontinuity and poor texture detail occurred in image inpainting method, the image inpainting algorithm based on self-adaptive group structure has proposed in this paper. The conception of self-adaptive group structure is different from traditional image patching operation and fixed group structure, which refers to the fact that a patch on the structure has fewer similar patches than the one within the textured region. A self-adaptive dictionary as well as the sparse representation model was established in the domain of self-adaptive group. Finally, the target cost function was solved by Split Bregman Iterational operation. The experimental results on target removing with Criminisi’s algorithm, GSR’s algorithm and SALSA’s algorithm in image pixels losting of image inpainting had shown that the proposed algorithm has better performance than other algorithms.


Image inpainting algorithm Sparse representation method Self-adaptive group structure Dictionary learning method 



This work is supported by the National Natural Science Foundation of China (No. 61402053, No. 51408069), the Science and Technology Service Platform of Hunan Province (No. 2012TP1001).


  1. 1.
    Guillemot, C., Le Meur, O.: Image inpainting: overview and recent advances. IEEE Signal Process. Mag. 31, 127–144 (2014)CrossRefGoogle Scholar
  2. 2.
    Chan, T., Shen, J.: Local inpainting models and tv inpainting. SIAM J. Appl. Math. 62, 1019–1043 (2001)MathSciNetGoogle Scholar
  3. 3.
    Chan, T.F., Shen, J.: Non-texture inpainting by curvature-driven diffusions. J. Vis. Commun. Image Represent. 12, 436–449 (2001)CrossRefGoogle Scholar
  4. 4.
    Ram, I., Elad, M., Cohen, I.: Image processing using smooth ordering of its patches. IEEE Trans. Image Process. 22, 2764–2774 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wong, A., Orchard, J.: A nonlocal-means approach to exemplar-based inpainting. Proceedings of the 15th IEEE International Conference on Image Processing. IEEE, 2008, pp. 2600–2603 (2008)Google Scholar
  6. 6.
    Criminisi, A., Perez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 13, 1200–1212 (2004)CrossRefGoogle Scholar
  7. 7.
    Shen, B., Hu, W., Zhang, Y., et al.: Image inpainting via sparse representation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009, pp. 697–700 (2009)Google Scholar
  8. 8.
    Li, Z., He, H., Yin, Z., et al.: A color-gradient patch sparsity based image inpainting algorithm with structure coherence and neighborhood consistency. Signal Process. 99, 116–128 (2014)CrossRefGoogle Scholar
  9. 9.
    Xu, Z., Sun, J.: Image inpainting by patch propagation using patch sparsity. IEEE Trans. Image Process. 19, 1153–1165 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Mairal, J., Bach, F., Ponce, J.m et al.: Non-local sparse models for image restoration. Proceedings of the IEEE 12th International Conference on Computer Vision. IEEE, 2009, pp. 2272–2279 (2009)Google Scholar
  11. 11.
    Zhang, J., Zhao, D., Gao, W.: Group-based sparse representation for image restoration. IEEE Trans. Image Process. 23, 3336–3351 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Xu, J., Zhang, L., Zuo, W., et al.: Patch group based non-local self-similarity prior learning for image de-noising. Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 244–252 (2015)Google Scholar
  13. 13.
    Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)CrossRefGoogle Scholar
  14. 14.
    Goldstein, T., Osher, S.: The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci. 2, 323–343 (2009)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Afonso, M.V., Bioucas-Dias, J.M., Figueiredo, M.A.T.: Fast image recovery using variable splitting and con-strained optimization. IEEE Trans. Image Process. 19, 2345–2356 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
  17. 17.
    Liu, H.P., Sun, F.C., Fang, B., Zhang, X.Y.: Robotic room-level localization using multiple sets of sonar measurements. IEEE Trans. Instrum. Meas. 66(1), 2–13 (2017)CrossRefGoogle Scholar
  18. 18.
    Liu, H.P., Guo, D., Sun, F.C.: Object recognition using tactile measurements: Kernel sparse coding methods. IEEE Trans. Instrum. Meas. 65(3), 656–665 (2016)CrossRefGoogle Scholar
  19. 19.
    Liu, H.P., Liu, Y.P., Sun, F.C.: Robust exemplar extraction using structured sparse coding. IEEE Trans. Neural Netw. Learn. Syst. 26(8), 1816–1821 (2015)MathSciNetCrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Energy and Power EngineeringChangsha University of Science and TechnologyChangshaPeople’s Republic of China

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