Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 25, Issue 5, Pages 2288-2297Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2016.2541959
Keywords
Empirical mode decomposition; unconstrained optimization; data-driven; non-stationary image
Funding
- National Scientific and Technical Research Council
- Universidad Nacional de Entre Rios [PID 6136]
- Agencia Nacional de Promocion Cientifica y Tecnologica [PICT 2012-2954]
- Center for Identification Technology Research
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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).
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