Fast Hole-Filling in Images Via Fast Comparison of Incomplete Patches

  • A. Averbuch
  • G. Gelles
  • A. Schclar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)


We present an algorithm for fast filling of missing regions (holes) in images. Holes may be the result of various causes: manual manipulation e.g. removal of an object from an image, errors in the transmission of an image or video, etc. The hole is filled one pixel at a time by comparing the neighborhood of each pixel to other areas in the image. Similar areas are used as clues for choosing the color of the pixel. The neighborhood and the areas that are compared are square shaped. This symmetric shape allows the hole to be filled in an evenly fashion. However, since square areas inside the hole include some uncolored pixels, we introduce a fast and efficient data structure which allows fast comparison of areas, even with partially missing data. The speed is achieved by using a two phase algorithm: a learning phase which can be done offline and a fast synthesis phase. The data structure uses the fact that colors in an image can be represented by a bounded natural number. The algorithm fills the hole from the boundaries inward, in a spiral form to produce a smooth and coherent result.


Query Process Learning Phase Texture Synthesis Indicator Vector Spiral Form 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of ACM SIGGRAPH, pp. 417–424. ACM Press, New York (2000)Google Scholar
  2. 2.
    Bertalmio, M., Vese, L., Sapiro, G., Osher, S.: Simultaneous structure and texture imag inpainting. UCLA CAM Report 02(47) (2002)Google Scholar
  3. 3.
    Chan, T., Shen, J.: Mathematical models for local nontexture inpaintings. SIAM Journal of Applied Mathematics 62(3), 1019–1043 (2001)MathSciNetGoogle Scholar
  4. 4.
    Efros, A.A., Leung, T.: Texture synthesis by non-parametric sampling. In: IEEE International Conference on Computer Vision, pp. 1033–1038 (1999)Google Scholar
  5. 5.
    Heeger, D.J., Bergen, J.R.: Pyramid-based texture analysis/synthesis. In: Proceedings of ACM SIGGRAPH, pp. 229–238 (1995)Google Scholar
  6. 6.
    Igehy, H., Pereira, L.: Image replacement through texture synthesis. In: Proceedings of the 1997 International Conference on Image Processing, vol. 3, p. 186 (1997)Google Scholar
  7. 7.
    Wei, L.Y., Levoy, M.: Fast texture synthesis using tree-structured vector quantization. In: Proceedings of ACM SIGGRAPH, pp. 479–488. ACM Press, New York (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. Averbuch
    • 1
  • G. Gelles
    • 1
  • A. Schclar
    • 1
  1. 1.School of Computer ScienceTel Aviv UniversityTel AvivIsrael

Personalised recommendations