Normalized Averaging Using Adaptive Applicability Functions with Applications in Image Reconstruction from Sparsely and Randomly Sampled Data

  • Tuan Q. Pham
  • Lucas J. van Vliet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


In this paper we describe a new strategy for using local structure adaptive filtering in normalized convolution. The shape of the filter, used as the applicability function in the context of normalized convolution, adapts to the local image structure and avoids filtering across borders. The size of the filter is also adaptable to the local sample density to avoid unnecessary smoothing over high certainty regions. We compared our adaptive interpolation technique with conventional normalized averaging methods. We found that our strategy yields a result that is much closer to the original signal both visually and in terms of MSE, meanwhile retaining sharpness and improving the SNR.


Mean Square Error Uncertain Data Mean Square Error Normalize Gradient Magnitude Image Inpainting 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Tuan Q. Pham
    • 1
  • Lucas J. van Vliet
    • 1
  1. 1.Pattern Recognition GroupDelft University of TechnologyDelftThe Netherlands

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