The Edge Preserving Wiener Filter for Scalar and Tensor Valued Images

  • Kai Krajsek
  • Rudolf Mester
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


This contribution presents a variation of the Wiener filter criterion, i.e. minimizing the mean squared error, by combining it with the main principle of normalized convolution, i.e. the introduction of prior information in the filter process via the certainty map. Thus, we are able to optimize a filter according to the signal and noise characteristics while preserving edges in images. In spite of its low computational costs the proposed filter schemes outperforms state of the art filter methods working also in the spatial domain. Furthermore, the Wiener filter paradigm is extended from scalar valued data to tensor valued data.


Training Image Markov Chain Monte Carlo Method Additive Gaussian Noise Observation Matrix Edge Preserve 
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 2006

Authors and Affiliations

  • Kai Krajsek
    • 1
  • Rudolf Mester
    • 1
  1. 1.Visual Sensorics and Information Processing LabJ.W. Goethe UniversityFrankfurtGermany

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