Parameterfree information-preserving surface restoration

  • Uwe Weidner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 801)


In this paper we present an algorithm for parameterfree information-preserving surface restoration. The algorithm is designed for 2.5D and 3D surfaces. The basic idea is to extract noise and signal properties of the data simultaneously by variance-component estimation and use this information for filtering. The variance-component estimation delivers information on how to weigh the influence of the data dependent term and the stabilizing term in regularization techniques, and therefore no parameter which controls this relation has to be set by the user.


Test Image Principal Curvature Convolution Kernel Regularization Technique Noise Reduction 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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Uwe Weidner
    • 1
  1. 1.Institut für PhotogrammetrieBonn

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