Generalized multichannel image-filtering structures

Recent works in multispectral image processing advocate the employment of vector approaches for this class of signals. Vector processing operators that involve the minimization of a suitable error criterion have been proposed and shown appropriate for this task. In this framework, two main classes of vector processing filters have been reported in the literature. In [1], Astola et al. introduce the well-known class of vector median filters (VMF), which are derived as maximum likelihood (ML) estimates from exponential distributions. In [2] and [3], the authors study the processing of color image data using directional information, considering the class of vector directional filters (VDF). In this paper, we introduce a new filter structure, the directional-distance filters (DDF), which combine both VDF and VMF in a novel way.We show that DDF are robust signal estimators under various noise distributions, they have the property of chromaticity preservation and, finally, compare favorably to other multichannel image processing filters.