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Self-adaptive image denoising based on bidimensional empirical mode decomposition (BEMD)

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 3rd International Conference on Biomedical Engineering and Biotechnology, 25–28 September 2014, Beijing, China

Article type: Research Article

Authors: Guo, Song; | Luan, Fangjun | Song, Xiaoyu | Li, Changyou

Affiliations: Information & Control Engineering Faculty, Shenyang Jianzhu University, Shenyang, China | School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China

Note: [] Corresponding author: Guo Song, Information & Control Engineering Faculty, Shenyang Jianzhu University, No. 9, Hunnan East Road, Hunnan New District, Shenyang City, Liaoning, China. Tel.: +86 24 24690666; Fax: +86 24 24690666; E-mail: [email protected].

Keywords: Image denoising, BEMD, self-adaption, Gaussian white noise, energy

DOI: 10.3233/BME-141143

Journal: Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 3215-3222, 2014

Published: 2014
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Abstract

To better analyze images with the Gaussian white noise, it is necessary to remove the noise before image processing. In this paper, we propose a self-adaptive image denoising method based on bidimensional empirical mode decomposition (BEMD). Firstly, normal probability plot confirms that 2D-IMF of Gaussian white noise images decomposed by BEMD follow the normal distribution. Secondly, energy estimation equation of the ith 2D-IMF (i=2,3,4,......) is proposed referencing that of ith IMF (i=2,3,4,......) obtained by empirical mode decomposition (EMD). Thirdly, the self-adaptive threshold of each 2D-IMF is calculated. Eventually, the algorithm of the self-adaptive image denoising method based on BEMD is described. From the practical perspective, this is applied for denoising of the magnetic resonance images (MRI) of the brain. And the results show it has a better denoising performance compared with other methods.

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