Abstract
Mathematical morphology is a popular framework for non-linear image processing, first introduced for binary and gray-level images, then extended to color and multivariate images. Various pseudo-morphological frameworks have been proposed as solutions to the problem of ordering multivariate data. We propose an improvement of the existing color probabilistic pseudo-morphology by computing the pseudo-extrema of a color set in a faster way, leading to a smaller execution time. We show the usefulness of the new construction in the context of noise reduction in color images using an OCCO filter, by comparing our approach with a series of color morphologies and pseudo-morphologies.
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