Abstract :
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. Astola et al. (1990) introduce the well-known class of vector median filters (VMF), which are derived as maximum likelihood (ML) estimates from exponential distributions. Trahanias et al. (see ibid., vol.2, no.4, p.528-34, 1993 and vol.5, no.6, p.868-80, 1996) study the processing of color image data using directional information, considering the class of vector directional filters (VDF). 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
Keywords :
exponential distribution; filtering theory; image colour analysis; maximum likelihood estimation; median filters; noise; spectral analysis; chromaticity preservation; color image data processing; directional distance filters; directional information; error criterion minimization; exponential distributions; filter structure; generalized multichannel image filtering; maximum likelihood estimates; multichannel image processing filters; noise distributions; robust signal estimators; vector approaches; vector directional filters; vector median filters; vector processing filters; vector processing operators; Color; Employment; Exponential distribution; Image processing; Information filtering; Information filters; Maximum likelihood estimation; Multispectral imaging; Noise robustness; Signal processing;
