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<p>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 that 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 white eliminating the noise. The newly developed adaptive weighted median filter is based on a 3/spl times/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.</p>
adaptive filters; minimisation; filtering and prediction theory; two-dimensional digital filters; Gibbs random field model based weight selection; 2-D adaptive weighted median filter; generalized filtering method; energy minimisation; linear filters; 3/spl times/3 square neighborhood structure; clique energies; 3-pixel cliques; 2-pixel cliques; local statistics

L. Onural, M. B. Alp and M. Gürelli, "Gibbs Random Field Model Based Weight Selection for the 2-D Adaptive Weighted Median Filter," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 831-837, 1994.
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