Gibbs random field model based weight selection for the 2-D adaptive weighted median filter
Date
1994Source 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
ArticleItem Usage Stats
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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.