Multichannel permutation filters

Vector order statistic based filters designed to exploit the correlation between the channels of a multichannel signal offer improved performance over purely marginal processing. In this paper, the class of permutation filters are extended to handle multichannel signals. Permutation filters are well suited for image processing; they are robust, able to preserve details, and edges in image and video signals, and can also model complicated non-linear systems accurately. Multichannel permutation filters are based on the permutation of a set of sequential observations from a multidimensional process. There exists no unique method for the ranking of vectors, so we evaluate two ranking formulations that preserve characteristics useful in constructing estimators. Simulation results for a video restoration problem are presented