Fast and Robust Edge-Guided Exemplar-Based Image Inpainting

  • Yun Wu
  • Chun Yuan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


A fast and robust edge-guide exemplar-based method of image inpainting is proposed in this paper. Unlike traditional exemplar-based methods, we introduce an edge-reconstruction procedure before inpainting textures. The edge reconstruction procedure exploits different properties of edges, such as the curvature similarity, color similarity, and other estimate of how well two edges connect to each other. Guided by the reconstructed edge lines, the improved exemplar-based method is used to restore the textures and remaining structures. Moreover, we redesign the random search strategy to make it more suitable for our framework to solve the time-consuming problem caused by exhaustive search in most exemplar-based methods. After the match patch is chosen, color transfer is used to propagate the match patch information to further improve the visual quality and perceptual reasonability. Comprehensive experiments are performed to compare the proposed method with other well-known methods on synthetic images and natural images. The results show the proposed method can not only greatly reduce the computing time of exemplar-based methods, but also behave better on visual plausibility and continuity.


image inpainting edge exemplar-based PatchMatch image completion 


  1. 1.
    Bertalmio, M., Saxpiro, G., Caselles, V., Ballester, C.: Image inpainting. In: ACM Comput. Graph (SIGGRAPH 2000), pp. 412–424 (2000)Google Scholar
  2. 2.
    Chan, T.F., Shen, J.: Mathematical models for local non-texture inpaintings. SIAM J. Appl. Math. 62, 1019–1043 (2001)MathSciNetGoogle Scholar
  3. 3.
    Chan, T.F., Shen, J.: Nontexture Inpainting by Curvature-Driven Diffusions. Journal of Visual Communication and Image Representaion 12, 436–449 (2001)CrossRefGoogle Scholar
  4. 4.
    Criminisi, A., Perez, P., Toyama, K.: Region Filling and Object Removal by Exemplar-Based Image Inpainting. IEEE Transaction on Image Processing 13, 1200–1212 (2004)CrossRefGoogle Scholar
  5. 5.
    Cheng, W.H., Hsieh, C.W., Lin, S.K., Wu, J.L.: Robust Algorithm for Exemplar-based Image Inpainting. In: Proc. Int. Conf. Comput. Graphics, Imaging Vis (CGIV), pp. 64–69 (2005)Google Scholar
  6. 6.
    Tae-o-sot, S., Nishhara, A.: Exemplar-based image inpainting with patch shifting scheme. In: 17th International Conference on Digital Signal Processing, pp. 1–5 (2011)Google Scholar
  7. 7.
    Kwok, T.H., Sheung, H., Wang, C.C.: Fast Query for Exemplar-Based Image Completion. IEEE Transaction on Image Processing 19(12), 3106–3115 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Transaction on Graphics 28(24) (2009)Google Scholar
  9. 9.
    Tae-o-sot, S., Nishihara, A.: Iterative gradient-driven patch-based inpainting. In: Ho, Y.-S. (ed.) PSIVT 2011, Part II. LNCS, vol. 7088, pp. 71–81. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Chen, Y., Luan, Q., Li, H.Q., Au, O.: Sketch-Guided Texture-Based Image Inpainting. In: Proc. IEEE International Conference on Image Processing, pp. 1997–2000 (2006)Google Scholar
  11. 11.
    Cheng, Y.: Mean Shift, Mode Seeking, and Clustering. IEEE Transaction on Pattern Analysis and Machine Intelligence 17(8), 790–799 (1995)CrossRefGoogle Scholar
  12. 12.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Transaction on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)CrossRefGoogle Scholar
  13. 13.
    Piegl, L., Tiller, W.: The NURBS Book. Springer (1995-1997)Google Scholar
  14. 14.
    Wexler, Y., Shechtman, E., Irani, M.: Space-Time Completion of Video. IEEE Transaction on Pattern Analysis and Machine Intelligence 29(3), 463–476 (2007)CrossRefGoogle Scholar
  15. 15.
    Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Computer Graphics and Applications 21, 34–41 (2001)CrossRefGoogle Scholar
  16. 16.
    Zhao, G.Y., Xiang, S.M., Li, H.: Application of Higher Moments in Color Transfer between Images. Journal of Computer-Aided Design & Computer Graphics 16(1) (2004)Google Scholar
  17. 17.
    Gao, H.X.: Statistic Computation [M]. Beijing Peking University Press (1995) (in Chinese) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yun Wu
    • 1
  • Chun Yuan
    • 1
  1. 1.Tsinghua-CUHK Joint Research Center for Media Sciences, Technologies and Systems Graduate School at ShenzhenTsinghua UniversityChina

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