Optimized curvelet-based empirical mode decomposition

Renjie Wu, Qieshi Zhang, Sei Ichiro Kamata

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationSeventh International Conference on Machine Vision, ICMV 2014
EditorsBranislav Vuksanovic, Jianhong Zhou, Antanas Verikas, Petia Radeva
ISBN (Electronic)9781628415605
Publication statusPublished - 2015
Event7th International Conference on Machine Vision, ICMV 2014 - Milan, Italy
Duration: 2014 Nov 192014 Nov 21

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference7th International Conference on Machine Vision, ICMV 2014


  • Empirical mode decomposition
  • Hilbert huang transform
  • Intrinsic mode functions
  • Second generation discrete Curvelet transform
  • Time-frequency analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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