GENERALIZED VECTOR MEDIANS FOR CORRELATED CHANNELS (FriAmSS1)

Author(s) : Yinbo Li (Department of Electrical and Computer Engineering, University of Delaware, US) Gonzalo Arce (Department of Electrical and Computer Engineering, University of Delaware, US) Jan Bacca (Department of Electrical and Computer Engineering, University of Delaware, US)

Abstract : Inspired by the maximum likelihood (ML) estimates of location in multivariate spaces, we introduce in this paper a new filtering structure capable of capturing and exploiting both spatial and cross-channel correlations embedded in the data. An adaptive optimization algorithm for a sub-optimal realization of the proposed generalized vector median (GVM) filter, namely the marginal GVM, is derived. The effectiveness of the algorithm is shown through a color image denoising experiment.

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