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

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Abstract

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.

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References

  1. Guillemot, C., Le Meur, O.: Image inpainting: overview and recent advances. IEEE Signal Process. Mag. 31, 127–144 (2014)

    Article  Google Scholar 

  2. Chan, T., Shen, J.: Local inpainting models and tv inpainting. SIAM J. Appl. Math. 62, 1019–1043 (2001)

    MathSciNet  Google Scholar 

  3. Chan, T.F., Shen, J.: Non-texture inpainting by curvature-driven diffusions. J. Vis. Commun. Image Represent. 12, 436–449 (2001)

    Article  Google Scholar 

  4. Ram, I., Elad, M., Cohen, I.: Image processing using smooth ordering of its patches. IEEE Trans. Image Process. 22, 2764–2774 (2013)

    Article  MathSciNet  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  9. Xu, Z., Sun, J.: Image inpainting by patch propagation using patch sparsity. IEEE Trans. Image Process. 19, 1153–1165 (2010)

    Article  MathSciNet  Google Scholar 

  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)

  11. Zhang, J., Zhao, D., Gao, W.: Group-based sparse representation for image restoration. IEEE Trans. Image Process. 23, 3336–3351 (2014)

    Article  MathSciNet  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  14. Goldstein, T., Osher, S.: The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci. 2, 323–343 (2009)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  16. http://sipi.usc.edu/database/

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

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).

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  1. School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha, People’s Republic of China

    Jiangchun Mo & Yucai Zhou

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  1. Jiangchun Mo
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  2. Yucai Zhou
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Correspondence to Jiangchun Mo.

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Mo, J., Zhou, Y. The research of image inpainting algorithm using self-adaptive group structure and sparse representation. Cluster Comput 22 (Suppl 3), 7593–7601 (2019). https://doi.org/10.1007/s10586-018-2323-8

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  • DOI: https://doi.org/10.1007/s10586-018-2323-8

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