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Improved bi-dimensional empirical mode decomposition based on 2D-assisted signals: analysis and application

Improved bi-dimensional empirical mode decomposition based on 2D-assisted signals: analysis and application

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Mode mixing, boundary effects, necessary extrema lacking and so on are the main problems involved in bi-dimensional empirical mode decomposition (BEMD). The study presents an improved BEMD based on 2D-assisted signals: 2D Gaussian noises. Firstly, the given 2D Gaussian noise and its negative counterpart are added to the original image, respectively, to construct the two images to be decomposed. Secondly, the decomposed intrinsic mode functions (IMFs) from the two images are added together to obtain the IMFs, in which the added noises are cancelled out with less mode mixing and boundary effects. The other contribution of the method lies in its overcoming of the problem of necessary extrema lacking that the previous BEMD fails. Some instructive conclusions are obtained in the improved BEMD. Lastly, the efficiency and performance of the method are given through theoretical analysis and its application in image enhancement, which outperforms some previous approaches.

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