An improved bidimensional empirical mode decomposition: A mean approach for fast decomposition

Chin Yu Chen, Shu Mei Guo, Wei Sheng Chang, Jason Sheng Hong Tsai, Kuo Sheng Cheng

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


In this paper, a mean approach is proposed to accelerate bidimensional empirical mode decomposition (BEMD). In the envelope generation process, the proposed method uses a modified mean filter to approximate the interpolated envelope of the conventional BEMD, and utilizes a convolution algorithm based on singular value decomposition (SVD) to further reduce the computation time. Order statistics filter width determination, originally used in fast and adaptive bidimensional empirical mode decomposition (FABEMD), is applied to adaptively formulate an envelope. Considering the computation efficiency, the proposed method improves the algorithm for calculating distances among extrema by using Delaunay triangulation (DT). The experimental results show that the mean approach can produce intrinsic mode functions faster than FABEMD, while retaining acceptable quality.

Original languageEnglish
Pages (from-to)344-358
Number of pages15
JournalSignal Processing
Publication statusPublished - 2014 May

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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