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Image denoising based on improved bidimensional empirical mode decomposition thresholding technology

  • Di Liu
  • Xiyuan Chen
Article
  • 13 Downloads

Abstract

In this paper, a novel image denoising methodology based on improved bidimensional empirical mode decomposition and soft interval thresholding technique is proposed. First, a noise compressed image is constructed. Then, the noise compressed image is decomposed by means of bidimensional empirical mode decomposition (BEMD) into a series of intrinsic mode functions (IMFs), which are separated into signal-dominant IMFs and noise-dominant IMFs using a similarity measure based on ℓ2-norm and a probability density function, and a soft interval thresholding technique is used adaptively to remove the noise inherent in noise-dominant IMFs. Finally, a denoised image is reconstructed by combining the signal-dominant IMFs and the denoised noise-dominant IMFs. The performance of the proposed denoising method is evaluated by using multiple images with different types of noise, and results from the proposed method are compared with those of other conventional methods in various noisy environments. Simulation results demonstrate that the proposed denoising method outperforms other denoising methods in terms of peak signal-to-noise ratio, mean square error and energy of the first IMF.

Keywords

BEMD Image denoising Noise compression 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 51375087, 51405203) and the Transformation Program of Science and Technology Achievements of Jiangsu Province (Grant No. BA2016139).

Compliance with ethical standards

Conflicts of interest

The authors declare no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education, School of Instrument Science and EngineeringSoutheast UniversityNanjingChina

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