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      • Department of Electrical and Electronics Engineering
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      Gibbs random field model based weight selection for the 2-D adaptive weighted median filter

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      Author(s)
      Onural, L.
      Alp, M. B.
      Gürelli, M. I.
      Date
      1994
      Source Title
      IEEE Transactions on Pattern Analysis and Machine Intelligence
      Print ISSN
      0162-8828
      Publisher
      IEEE
      Volume
      16
      Issue
      8
      Pages
      831 - 837
      Language
      English
      Type
      Article
      Item Usage Stats
      108
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      92
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      Abstract
      A generalized filtering method based on the minimization of the energy of the Gibbs model is described. The well-known linear and median filters are all special cases of this method. It is shown that, with the selection of appropriate energy functions, the method can be successfully used to adapt the weights of the adaptive weighted median filter to preserve different textures within the image while eliminating the noise. The newly developed adaptive weighted median filter is based on a 3 x 3 square neighborhood structure. The weights of the pixels are adapted according to the clique energies within this neighborhood structure. The assigned energies to 2- or 3-pixel cliques are based on the local statistics within a larger estimation window. It is shown that the proposed filter performance is better compared to some well-known similar filters like the standard, separable, weighted and some adaptive weighted median filters.
      Keywords
      Gihbs random field model
      Adaptive filtering
      Weighted median filter
      Image noise filtering
      Permalink
      http://hdl.handle.net/11693/13605
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/34.308480
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      • Department of Electrical and Electronics Engineering 3702
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