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A pixel-level entropy-weighted image fusion algorithm based on bidimensional ensemble empirical mode decomposition

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  • Pei Wang
  • Hui Fu
  • Ke Zhang

Abstract

The bidimensional empirical mode decomposition algorithm is more suitable to handle image fusion than the traditional multi-scale decomposition methods in the image fusion area. Nevertheless, there are several inherent problems of empirical mode decomposition, such as the mode mixing problem or end effects problem. As an improved empirical mode decomposition method, the ensemble empirical mode decomposition improves the empirical mode decomposition, by averaging the modes of all noise-added signals, in order to improve the mode mixing problem. In this article, an adaptive image fusion algorithm based on the representation of bidimensional ensemble empirical mode decomposition is proposed. This novel algorithm decomposes the source image by the bidimensional ensemble empirical mode decomposition algorithm, and a pixel-level weighting fusion method is then presented based on the entropy of intrinsic mode function; the fusion image can thus be obtained by inversing bidimensional ensemble empirical mode decomposition on the composite representation. Based on the quantitative comparison results, the proposed algorithm provides fusion performance to the Laplacian pyramid and wavelet transform methods. In addition, the proposed algorithm has adaptive capabilities and does not need any predetermined filters or wavelet functions.

Suggested Citation

  • Pei Wang & Hui Fu & Ke Zhang, 2018. "A pixel-level entropy-weighted image fusion algorithm based on bidimensional ensemble empirical mode decomposition," International Journal of Distributed Sensor Networks, , vol. 14(12), pages 15501477188, December.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:12:p:1550147718818755
    DOI: 10.1177/1550147718818755
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    References listed on IDEAS

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    1. Zhou, Daming & Gao, Fei & Breaz, Elena & Ravey, Alexandre & Miraoui, Abdellatif, 2017. "Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach," Energy, Elsevier, vol. 138(C), pages 1175-1186.
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    Cited by:

    1. B. Srivani & N. Sandhya & B. Padmaja Rani, 2024. "A case study for performance analysis of big data stream classification using spark architecture," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(1), pages 253-266, January.

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