Skip to main content

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 245))

Abstract

In this paper we have proposed a novel method of removing random valued impulse noises from the digital images. A variable window such as 5 × 5 and 3 × 3 are utilized for such purpose. The proposed method is a switching median filter. The detection of noises in every 5 × 5 window of the noisy image is done using all neighbor directional weighted pixels. After detection of noisy pixel in the 5 × 5 window the proposed filtering scheme restored it to a pixel which is most suitable in the 3 × 3 and 5 × 5 window regions. This scheme is based on weighted median filtering on the 3 × 3 window regional pixels. Three user parameters of the proposed noise removal operator are searched in a 3D space using a randomized search and optimization technique i.e., Genetic Algorithm. Implementation of the scheme shows better noise removal performance and also preserves the image fine details well.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abreu, E., Lightstone, M., Mitra, S.K., Arakawa, K.: A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE Transactions on Image Processing 5(6), 1012–1025 (1996)

    Article  Google Scholar 

  2. Brownrigg, D.R.K.: The weighted median filter. Communications of the ACM 27(8), 807–818 (1984)

    Article  Google Scholar 

  3. Chen, T., Ma, K., Chen, L.: Tri-state median filter for image de noising. IEEE Transaction Image Processing 8(12), 1834–1838 (1999)

    Article  Google Scholar 

  4. Chen, T., Wu, H.R.: Adaptive impulse detection using center weighted median filters. IEEE Signal Processing Letters 8(1), 1–3 (2001)

    Article  Google Scholar 

  5. Chen, T., Wu, H.R.: Space variant median filters for the restoration of impulse noise corrupted images. IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing 48(8), 784–789 (2001)

    Article  MATH  Google Scholar 

  6. Crnojevic, V., Senk, V., Trpovski, Z.: Advanced impulse detection based on pixel- wise mad. IEEE Signal Processing Letters 11(7), 589–592 (2004)

    Article  Google Scholar 

  7. Goldberg, D.E.: Genetic algorithm in search, optimization and machine learning. Addison- Wesley (1989)

    Google Scholar 

  8. Dong, Y., Xu, S.: A new directional weighted median filter for removal of random - valued impulse noise. IEEE Signal Processing Letters 14(3), 193–196 (2007)

    Article  Google Scholar 

  9. Forouzan, A.R., Araabi, B.: Iterative median filtering for restoration of images with impulsive noise. Electronics, Circuits and Systems 1, 232–235 (2003)

    Google Scholar 

  10. Ko, S.J., Lee, Y.H.: Center weighted median filters and their applications to image enhancement. IEEE Transactions on Circuits and Systems 38(9), 984–993 (2001)

    Article  Google Scholar 

  11. Kong, H., Guan, L.: A neural network adaptive filter for the removal of impulse noise in digital images. Neural Networks Letters 9(3), 373–378 (1996)

    Article  Google Scholar 

  12. Mandal, J.K., Sarkar, A.: A novel modified directional weighted median based filter for removal of random valued impulse noise. In: International Symposium on Electronic System Design, pp. 230–234 (December 2010)

    Google Scholar 

  13. Mandal, J.K., Sarkar, A.: A modified weighted based filter for removal of random impulse noise. In: Second International Conference on Emerging Applications of Information Technology, pp. 173–176 (February 2011)

    Google Scholar 

  14. Russo, F., Ramponi, G.: A fuzzy filter for images corrupted by impulse noise. IEEE Signal Processing Letter 3, 168–170 (1996)

    Article  Google Scholar 

  15. Sa, P.K., Dash, R., Majhi, B.: Second order difference based detection and directional weighted median filter for removal of random valued impulsive noise. IEEE Signal Processing Letters, 362–364 (December 2009)

    Google Scholar 

  16. Wang, Z., Zhang, D.: Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Circuits and Systems 46(1), 78–80 (1999)

    Article  Google Scholar 

  17. Michalewicz, Z.: Genetic algorithms +data structures = evolution programms. Springer, Heidelberg (1996)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mandal, J.K., Mukhopadhyay, S. (2011). GA Based Denoising of Impulses (GADI). In: Chaki, N., Cortesi, A. (eds) Computer Information Systems – Analysis and Technologies. Communications in Computer and Information Science, vol 245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27245-5_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27245-5_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27244-8

  • Online ISBN: 978-3-642-27245-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics