Improved probabilistic pseudo‐morphology for noise reduction in colour images
Radu‐Mihai Coliban, Mihai Ivanovici, Noël Richard · IET Image Processing · 2016
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.