Image Denoising Based on Neutrosophic Wiener Filtering

  • J. Mohan
  • A. P. Thilaga Shri Chandra
  • V. Krishnaveni
  • Yanhui Guo
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)

Abstract

This paper proposes an image denoising technique based on Neutrosophic Set approach of wiener filtering. A Neutrosophic Set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. Here the image is transformed into NS domain, which is described using three membership sets: True (T), Indeterminacy (I) and False (F). The entropy of the neutrosophic set is defined and employed to evaluate the indeterminacy. The ω-wiener filtering operation is used on T and F to decrease the set indeterminacy and remove noise. We have conducted experiments on a variety of noisy images using different types of noises with different levels. The experimental results demonstrate that the proposed approach can remove noise automatically and effectively. Especially, it can process not only noisy images with different levels of noise, but also images with different kinds of noise well without knowing the type of the noise.

Keywords

Image denoising Neutrosophic Set Wiener filtering Entropy PSNR 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cheng, H.D., Guo, Y.: A new neutrosophic approach to image thresholding. New Mathemetics and Natural Computation 4(3), 291–308 (2008)CrossRefGoogle Scholar
  2. 2.
    Guo, Y., Cheng, H.D.: New Neutrosophic approach to image segmentation. Pattern Recognition 42(5), 587–595 (2009)MATHCrossRefGoogle Scholar
  3. 3.
    Zhang, M., Zhang, L., Cheng, H.D.: A Neutrosophic approach to image segmentation based on watershed method. Signal Processing 90(5), 1510–1517 (2010)MATHCrossRefGoogle Scholar
  4. 4.
    Guo, Y., Cheng, H.D., Zhang, Y.: A New Neutrosophic approach to Image Denoising. New Mathemetics and Natural Computation 5(3), 653–662 (2009)MATHCrossRefGoogle Scholar
  5. 5.
    Samarandache, F.: A unifying field in logics Neutrosophic logic. In: Neutrosophic Set, Neutrosophic Probability, 3rd edn. American Research Press (2003)Google Scholar
  6. 6.
    Zadeh, L.A.: Fuzzy sets. Inform and Control 8, 338–353 (1965)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Turksen, I.: Interval valued fuzzy sets based on normal forms. Fuzzy Sets and Systems 20, 191–210 (1986)MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Atanassov, K.: Intuitionistic fuzzy sets. Fuzzy Sets and Systems 20, 87–96 (1986)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Atanassov, K.: More on Intuitionistic fuzzy sets. Fuzzy Sets and Systems 33, 37–46 (1989)MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • J. Mohan
    • 1
  • A. P. Thilaga Shri Chandra
    • 2
  • V. Krishnaveni
    • 1
  • Yanhui Guo
    • 3
  1. 1.Department of Electronics and Communication EngineeringP.S.G. College of TechnologyCoimbatoreIndia
  2. 2.Department of Electronics and Communication EngineeringSri Krishna College of Engineering and TechnologyCoimbatoreIndia
  3. 3.Department of RadiologyUniversity of MichiganAnn ArborUSA

Personalised recommendations