• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Bi-dimensional Empirical Mode Decomposition Algorithm Based on Particle Swarm-Fractal Interpolation

Vol. 12, No.12, December 31, 2018
10.3837/tiis.2018.12.019, Download Paper (Free):

Abstract

Performance of the interpolation algorithm used in the technique of bi-dimensional empirical mode decomposition directly affects its popularization and application, so that the researchers pay more attention to the algorithm reasonable, accurate and fast. However, it has been a lack of an adaptive interpolation algorithm that is relatively satisfactory for the bi-dimensional empirical mode decomposition (BEMD) and is derived from the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. All parameters involved in the interpolation process are determined by using the particle swarm optimization algorithm. The presented interpolation algorithm can solve those problems of low efficiency and poor precision in the interpolation operation of bi-dimensional empirical mode decomposition and can also result in accurate and reliable bi-dimensional intrinsic modal functions with higher speed in the decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.


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Cite this article

[IEEE Style]
Feng-Ping An and Xin He, "Bi-dimensional Empirical Mode Decomposition Algorithm Based on Particle Swarm-Fractal Interpolation," KSII Transactions on Internet and Information Systems, vol. 12, no. 12, pp. 5955-5977, 2018. DOI: 10.3837/tiis.2018.12.019

[ACM Style]
An, F. and He, X. 2018. Bi-dimensional Empirical Mode Decomposition Algorithm Based on Particle Swarm-Fractal Interpolation. KSII Transactions on Internet and Information Systems, 12, 12, (2018), 5955-5977. DOI: 10.3837/tiis.2018.12.019