Orientation-Independent Empirical Mode Decomposition for Images Based on Unconstrained Optimization

MARCELO ALEJANDRO COLOMINAS; ANNE HUMEAU-HEURTIER; GASTÓN SCHLOTTHAUER

IEEE TRANSACTIONS ON IMAGE PROCESSING

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Lugar: New York; Año: 2016 vol. 25 p. 2288 - 2297

1057-7149

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 state of the art methods evidencing the advantages of being computationally efficient, orientation-independent, and leads to better performances for the decomposition of 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. Theproposed framework leaves room for an nD extension (n > 2).