Advertisement

Image Inpainting Based on Probabilistic Structure Estimation

  • Takashi Shibata
  • Akihiko Iketani
  • Shuji Senda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

A novel inpainting method based on probabilistic structure estimation has been developed. The method consists of two steps. First, an initial image, which captures rough structure and colors in the missing region, is estimated. This image is generated by probabilistically interpolating the gradient inside the missing region, and then by flooding the colors on the boundary into the missing region using Markov Random Field. Second, by locally replacing the missing region with local patches similar to both the adjacent patches and the initial image, the inpainted image is synthesized. Since the patch replacement process is guided by the initial image, the inpainted image is guaranteed to preserve the underlying structure. This also enables patches to be replaced in a greedy manner, i.e. without optimization. Experiments show the proposed method outperforms previous methods in terms of both subjective image quality and computational speed.

Keywords

Markov Random Field Initial Image Missing Region Subjective Image Quality Greedy Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: Patchmatch: A randomized correspondence algorithm for structural image editing. In: Proc. of ACM SIGGRAPH, vol. 29 (2009)Google Scholar
  2. 2.
    Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proc. of ACM SIGGRAPH, pp. 417–424 (2000)Google Scholar
  3. 3.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. on PAMI 23, 1222–1239 (2001)CrossRefGoogle Scholar
  4. 4.
    Chan, F.T., Shen, J.: Non-texture inpainting by curvature-driven diffusions. J. of VCIR 12, 436–449 (2001)Google Scholar
  5. 5.
    Chen, Y., Luan, Y., Li, H., Au, C.O.: Sketch-guided texture-based image inpainting. In: Proc. of ICIP, pp. 1997–2000 (2006)Google Scholar
  6. 6.
    Criminisi, A., Pérez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. IEEE Trans. on IP 13, 1200–1212 (2004)Google Scholar
  7. 7.
    Harrison, P.: A Non-hierarchical procedure for re-synthesis of complex texture. In: Proc. of WSCG, pp. 190–197 (2001)Google Scholar
  8. 8.
    Jia, J., Tang, K.C.: Image repairing: robust image synthesis by adaptive ND tensor voting. In: Proc. of CVPR, pp. 643–650 (2003)Google Scholar
  9. 9.
    Kawai, N., Sato, T., Yokoya, N.: Image inpainting considering brightness change and spatial locality of textures. In: Proc. of VISAPP, vol. 1, pp. 66–73 (2008)Google Scholar
  10. 10.
    Komodakis, N., Tziritas, G.: Image completion using efficient belief propagation via priority scheduling and dynamic pruning. IEEE Trans. on IP 16, 2649–2661 (2007)MathSciNetGoogle Scholar
  11. 11.
    Li, R.B., Qi, Y., Shen, K.X.: An image inpainting method. In: Conf. on CAD and Computer Graphics, pp. 531–536 (2005)Google Scholar
  12. 12.
    Pritch, Y., Kav-Venaki, E., Peleg, S.: Shift-map image editing. In: Proc. of ICCV, pp. 151–158 (2009)Google Scholar
  13. 13.
    Sun, J., Yuan, L., Jia, J., Shum, Y.H.: Image completion with structure propagation. In: Proc. of ACM SIGGRAPH, pp. 861–868 (2005)Google Scholar
  14. 14.
    Wexler, Y., Shechtman, E., Irani, M.: Space-time completion of video. IEEE Trans. of PAMI 29, 463–476 (2007)CrossRefGoogle Scholar
  15. 15.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Takashi Shibata
    • 1
  • Akihiko Iketani
    • 1
  • Shuji Senda
    • 1
  1. 1.NEC CorporationKawasakiJapan

Personalised recommendations