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International Journal of Antennas and Propagation
Volume 2015, Article ID 769478, 12 pages
Research Article

Enhancing Image Denoising Performance of Bidimensional Empirical Mode Decomposition by Improving the Edge Effect

1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2Department of Civil Engineering, North China Institute of Science and Technology, Yanjiao, Beijing 101601, China
3School of Government, Central University of Finance and Economics, Beijing 100081, China

Received 20 April 2015; Accepted 31 August 2015

Academic Editor: Atsushi Mase

Copyright © 2015 Feng-Ping An et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Bidimensional empirical mode decomposition (BEMD) algorithm, with high adaptive ability, provides a suitable tool for the noisy image processing, and, however, the edge effect involved in its operation gives rise to a problem—how to obtain reliable decomposition results to effectively remove noises from the image. Accordingly, we propose an approach to deal with the edge effect caused by BEMD in the decomposition of an image signal and then to enhance its denoising performance. This approach includes two steps, in which the first one is an extrapolation operation through the regression model constructed by the support vector machine (SVM) method with high generalization ability, based on the information of the original signal, and the second is an expansion by the closed-end mirror expansion technique with respect to the extrema nearest to and beyond the edge of the data resulting from the first operation. Applications to remove the Gaussian white noise, salt and pepper noise, and random noise from the noisy images show that the edge effect of the BEMD can be improved effectively by the proposed approach to meet requirement of the reliable decomposition results. They also illustrate a good denoising effect of the BEMD by improving the edge effect on the basis of the proposed approach. Additionally, the denoised image preserves information details sufficiently and also enlarges the peak signal-to-noise ratio.