Improved probabilistic pseudo‐morphology for noise reduction in colour images

Abstract

Mathematical morphology is a popular framework for non-linear image processing, first introduced for binary and grey-level images, then extended to colour and multivariate images. Various pseudo-morphologies have been proposed as solutions to the problem of ordering multivariate data. Despite the lack of some properties, pseudo-morphologies have proved useful in various applications, such as filtering or texture classification. The authors propose to improve the existing colour probabilistic pseudo-morphology by changing the way the local pseudo-extrema are chosen. They show the usefulness of the new construction in the context of noise reduction in colour images using the open-close close-open filter, by highlighting the improvement over the original construction and comparing the authors’ results with other relevant morphological and pseudo-morphological approaches.