Orientation-Independent Empirical Mode Decomposition for Images Based on Unconstrained Optimization
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 pr...
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| Veröffentlicht in: | IEEE transactions on image processing 2016-05, Vol.25 (5), p.2288-2297 |
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| container_title | IEEE transactions on image processing |
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| creator | Colominas, Marcelo A. Humeau-Heurtier, Anne Schlotthauer, Gaston |
| description | 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 ). |
| doi_str_mv | 10.1109/TIP.2016.2541959 |
| format | Article |
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| subjects | Computational efficiency data-driven Decomposition Empirical Mode Decomposition Engineering Sciences Frequency modulation Interpolation non-stationary image Optimization Oscillations Proposals Signal and Image processing Sparse matrices Splines (mathematics) State of the art Transaction processing Two dimensional unconstrained optimization |
| title | Orientation-Independent Empirical Mode Decomposition for Images Based on Unconstrained Optimization |
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