Generalized vector medians for correlated channels

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.