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Article Dans Une Revue IEEE Transactions on Image Processing Année : 2016

Orientation-independent empirical mode decomposition for images based on unconstrained optimization

Résumé

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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hal-01388824 , version 1 (22-06-2021)

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Marcelo Colominas, Anne Humeau-Heurtier, Gastòn Schlotthauer. Orientation-independent empirical mode decomposition for images based on unconstrained optimization. IEEE Transactions on Image Processing, 2016, 25 (5), pp.2288-2297. ⟨10.1109/TIP.2016.2541959⟩. ⟨hal-01388824⟩
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