Optimized curvelet-based empirical mode decomposition

Renjie Wu; Qieshi Zhang; Sei-ichiro Kamata

doi:10.1117/12.2180847

14 February 2015 Optimized curvelet-based empirical mode decomposition

Renjie Wu, Qieshi Zhang, Sei-ichiro Kamata

Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 94451O (2015) https://doi.org/10.1117/12.2180847
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy

Abstract

The recent years has seen immense improvement in the development of signal processing based on Curvelet transform. The Curvelet transform provide a new multi-resolution representation. The frame elements of Curvelets exhibit higher direction sensitivity and anisotropic than the Wavelets, multi-Wavelets, steerable pyramids, and so on. These features are based on the anisotropic notion of scaling. In practical instances, time series signals processing problem is often encountered. To solve this problem, the time-frequency analysis based methods are studied. However, the time-frequency analysis cannot always be trusted. Many of the new methods were proposed. The Empirical Mode Decomposition (EMD) is one of them, and widely used. The EMD aims to decompose into their building blocks functions that are the superposition of a reasonably small number of components, well separated in the time-frequency plane. And each component can be viewed as locally approximately harmonic. However, it cannot solve the problem of directionality of high-dimensional. A reallocated method of Curvelet transform (optimized Curvelet-based EMD) is proposed in this paper. We introduce a definition for a class of functions that can be viewed as a superposition of a reasonably small number of approximately harmonic components by optimized Curvelet family. We analyze this algorithm and demonstrate its results on data. The experimental results prove the effectiveness of our method.

Citation Download Citation

Renjie Wu, Qieshi Zhang, and Sei-ichiro Kamata "Optimized curvelet-based empirical mode decomposition", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 94451O (14 February 2015); https://doi.org/10.1117/12.2180847

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KEYWORDS

Time-frequency analysis

Wavelets

Magnetic resonance imaging

Head

Superposition

Signal processing

Wavelet transforms

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