title: Orientation-Independent Empirical Mode Decomposition for Images Based on Unconstrained Optimization.
abstract: This paper introduces a 2D extension of the empirical mode decomposition (EMD), through a novel approach based on unconstrained optimization. EMD is a fully data-driven method that locally separates, in a completely data-driven and unsupervised manner, signals into fast and slow oscillations. The present proposal implements the method in a very simple and fast way, and it is compared with the state-of-the-art methods evidencing the advantages of being computationally efficient, orientation-independent, and leads to better performances for the decomposition of amplitude modulated-frequency modulated (AM-FM) images. The resulting genuine 2D method is successfully tested on artificial AM-FM images and its capabilities are illustrated on a biomedical example. The proposed framework leaves room for an nD extension (n > 2 ).
authors.forename: Marcelo A
authors.initials: MA
authors.lastname: Colominas
authors.forename: Anne
authors.initials: A
authors.lastname: Humeau-Heurtier
authors.forename: Gastón
authors.initials: G
authors.lastname: Schlotthauer