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access icon openaccess Image denoising in bidimensional empirical mode decomposition domain: the role of Student's probability distribution function

Hybridisation of the bi-dimensional empirical mode decomposition (BEMD) with denoising techniques has been proposed in the literature as an effective approach for image denoising. In this Letter, the Student's probability density function is introduced in the computation of the mean envelope of the data during the BEMD sifting process to make it robust to values that are far from the mean. The resulting BEMD is denoted tBEMD. In order to show the effectiveness of the tBEMD, several image denoising techniques in tBEMD domain are employed; namely, fourth order partial differential equation (PDE), linear complex diffusion process (LCDP), non-linear complex diffusion process (NLCDP), and the discrete wavelet transform (DWT). Two biomedical images and a standard digital image were considered for experiments. The original images were corrupted with additive Gaussian noise with three different levels. Based on peak-signal-to-noise ratio, the experimental results show that PDE, LCDP, NLCDP, and DWT all perform better in the tBEMD than in the classical BEMD domain. It is also found that tBEMD is faster than classical BEMD when the noise level is low. When it is high, the computational cost in terms of processing time is similar. The effectiveness of the presented approach makes it promising for clinical applications.


    1. 1)
      • 3. Lahmiri, S., Boukadoum, M.: ‘An application of the empirical mode decomposition to brain magnetic resonance images classification’. Proc. IEEE LASCAS, 2013, pp. 14.
    2. 2)
      • 8. Yi, S., Zeng, C.: ‘DWI denoising method based on BEMD and adaptive wiener filter’. Proc. IEEE Int. Conf. on Biomedical Engineering and Informatics (BMEI), 2012, pp. 3034.
    3. 3)
      • 4. Lahmiri, S., Boukadoum, M.: ‘Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of intrinsic mode functions’, Biomed. Tech./Biomed. Eng., 2014, 59, pp. 357366.
    4. 4)
    5. 5)
      • 7. Pan, J.-J., Tang, Y.: ‘A new image denoising method based on BEMD and self-similar feature’. Proc. IEEE Int. Conf. on Wavelet Analysis and Pattern Recognition (ICWAPR), 2010, pp. 15.
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
      • 9. Liua, J., Shi, C., Gao, M.: ‘Image denoising based on BEMD and PDE’. Proc. IEEE Int. Conf. on Computer Research and Development (ICCRD), 2011, pp. 110112.
    11. 11)
      • 6. Lahmiri, S., Boukadoum, M.: ‘Pathology grading in retina digital images using Student-adjusted empirical mode decomposition and power law statistics’. Proc. IEEE LASCAS, 2015, pp. 14.
    12. 12)
    13. 13)
      • 2. Lahmiri, S., Boukadoum, M.: ‘Adjusted empirical mode decomposition with improved performance for signal modeling and prediction’. Proc. IEEE LASCAS, 2014, pp. 14.
    14. 14)
    15. 15)

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