Image Reconstruction by Prioritized Incremental Normalized Convolution

  • Anders Landström
  • Frida Nellros
  • Håkan Jonsson
  • Matthew Thurley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)


A priority-based method for pixel reconstruction and incremental hole filling in incomplete images and 3D surface data is presented. The method is primarily intended for reconstruction of occluded areas in 3D surfaces and makes use of a novel prioritizing scheme, based on a pixelwise defined confidence measure, that determines the order in which pixels are iteratively reconstructed. The actual reconstruction of individual pixels is performed by interpolation using normalized convolution.

The presented approach has been applied to the problem of reconstructing 3D surface data of a rock pile as well as randomly sampled image data. It is concluded that the method is not optimal in the latter case, but the results show an improvement to ordinary normalized convolution when applied to the rock data and are in this case comparable to those obtained from normalized convolution using adaptive neighborhood sizes.


image reconstruction hole filling normalized convolution 


  1. 1.
    Averbuch, A., Gelles, G., Schclar, A.: Fast hole-filling in images via fast comparison of incomplete patches. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) MRCS 2006. LNCS, vol. 4105, pp. 738–744. Springer, Heidelberg (2006)Google Scholar
  2. 2.
    Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2000, pp. 417–424. ACM Press, New York (2000)Google Scholar
  3. 3.
    Faille, F., Petrou, M.: Invariant image reconstruction from irregular samples and hexagonal grid splines. Image and Vision Computing 28(8), 1173–1183 (2010)CrossRefGoogle Scholar
  4. 4.
    Farnebäck, G.: Polynomial Expansion for Orientation and Motion Estimation. PhD thesis, Linköping University, Sweden (2002)Google Scholar
  5. 5.
    Ju, T.: Fixing geometric errors on polygonal models: A survey. Journal of Computer Science and Technology 24, 19–29 (2009)CrossRefGoogle Scholar
  6. 6.
    Knutsson, H., Westin, C.-F.: Normalized and differential convolution. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1993, pp. 515–523 (June 1993)Google Scholar
  7. 7.
    Pham, T.Q., van Vliet, L.J., Schutte, K.: Robust fusion of irregularly sampled data using adaptive normalized convolution. EURASIP J. Appl. Signal Process. 2006, 236–236 (2006)CrossRefGoogle Scholar
  8. 8.
    Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Transactions on Image Processing 16(2), 349–366 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Thurley, M.J., Ng, K.C.: Identifying, visualizing, and comparing regions in irregularly spaced 3D surface data. Computer Vision and Image Understanding 98(2), 239–270 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anders Landström
    • 1
  • Frida Nellros
    • 1
  • Håkan Jonsson
    • 1
  • Matthew Thurley
    • 1
  1. 1.Department of Computer Science, Electrical and Space EngineeringLuleå University of TechnologyLuleåSweden

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