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An adaptive fuzzy filter for image denoising

  • Weiping Zhang
  • Mohit Kumar
  • Jingzhi Yang
  • Yunfeng Zhou
  • Yihua Mao
Article
  • 69 Downloads

Abstract

This study considers the problem of fuzzy modeling of the images in pixel domain. A zero-order Takagi–Sugeno type fuzzy model provides fuzzy smoothing to the image intensities for removing the additive noise from an image. An adaptive fuzzy filtering algorithm is suggested for estimating the parameters of the fuzzy model with noisy image data. The mathematical analysis of the proposed filtering algorithm has been provided in both deterministic and stochastic framework. The deterministic robustness of the filtering algorithm was shown by deriving an upper bound on the magnitude of estimation errors. The fuzzy filtering algorithm doesn’t demand Gaussian assumption of the noise and is also optimal in the “sense” of variation Bayes towards Student-t distributed noises.

Keywords

Adaptive fuzzy filtering Robustness Image denoising Variational Bayes Student-t distribution 

Notes

Acknowledgments

Funding was provided by National Natural Science Foundation of China (ID0EQOAE4397).

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Copyright information

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

Authors and Affiliations

  • Weiping Zhang
    • 1
  • Mohit Kumar
    • 2
    • 4
  • Jingzhi Yang
    • 2
  • Yunfeng Zhou
    • 3
  • Yihua Mao
    • 3
  1. 1.Department of Electronic Information EngineeringNanchang UniversityNanchangChina
  2. 2.Binhai Industrial Technology Research Institute of Zhejiang UniversityTianjinChina
  3. 3.Zhejiang University College of Civil Engineering and ArchitectureHangzhouChina
  4. 4.Faculty of Computer Science and Electrical EngineeringUniversity of RostockRostockGermany

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