Skip to main content

Hybrid Sigma Filter for Processing Images Corrupted by Multiplicative Noise

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4179))

Abstract

A standard sigma filter proposed by J.-S. Lee has found wide applications and frequent implementations in software packages. Later, several modifications have been introduced in order to improve its performance. In this paper we propose some new modifications trying to combine advantages of the original sigma and local statistic Lee filter as well as to ensure the filter robustness with respect to impulse noise. The basic performance characteristics of the proposed hybrid sigma filter are studied for cases of pure multiplicative noise. The comparison to some other well known filters is performed. A real life example of the designed filter application to side-look radar image is given.

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 139.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Lee, J.S.: Digital Image Smoothing and the Sigma Filter. Computer Vision, Graphics and Image processing 24, 255–269 (1983)

    Article  Google Scholar 

  2. Tsymbal, O.B., Lukin, V.V., Ponomarenko, N.N., Zelensky, A.A., Egiazarian, K.O., Astola, J.T.: Three-state Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing. EURASIP Journal on Applied Signal Processing 8, 1185–1204 (2005)

    Google Scholar 

  3. http://www.rsinc.com/envi/

  4. Kurekin, A.A., Lukin, V.V., Zelensky, A.A., Tsymbal, O.V., Kulemin, G.P., Engman, E.T.: Processing multichannel radar images by modified vector sigma filter for soil erosion degree determination. In: Proceedings SPIE/EUROPTO Symposium on Aerospace Remote Sensing, SPIE, vol. 3868, pp. 412–423 (1999)

    Google Scholar 

  5. Lukac, R., Smolka, B., Plataniotis, K., Venetsanopoulos, A.: Generalized adaptive vector sigma filters. In: Proceedings of International Conference on Multimedia and Expo, vol. 1, pp. 537–540 (2003)

    Google Scholar 

  6. Lukac, R., Smolka, B., Plataniotis, K.N., Venetsanopoulos, A.N.: Vector Sigma Filters for Noise Detection and Removal in Color Images. Journal of Visual Communication and Image Representation 17(1), 1–26 (2006)

    Article  Google Scholar 

  7. Zelensky, A.A., Kulemin, G.P., Kurekin, A.A., Lukin, V.V., Tsymbal, O.V.: Modified Vector Sigma Filter for the Processing of Multichannel Radar Images and Increasing Reliability of Its Interpretation. Telecommunications and Radioengineering, Begell House 58(1-2), 100–113 (2002)

    Google Scholar 

  8. Lukin, V., Ponomarenko, N., Zelensky, A., Astola, J., Egiazarian, K.: Automatic Design of Locally Adaptive Filters for Pre-processing of Images, Subject to Further Interpretation. In: Proceedings of 2006 IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 41–45 (2006)

    Google Scholar 

  9. Lukin, V.V., Ponomarenko, N.N., Kuosmanen, P.S., Astola, J.T.: Modified Sigma Filter for Processing Images Corrupted by Multiplicative and Impulsive Noise. In: Proceedings of EUSIPCO 1996, vol. III, pp. 1909–1912 (1996)

    Google Scholar 

  10. Lukin, V.V., Ponomarenko, N.N., Zelensky, A.A., Kurekin, A.A., Astola, J.T., Koivisto, P.T.: Modified Sigma Filter with Improved Noise Suppression Efficiency and Spike Removal Ability. In: Proceedings of the 6th International Workshop on Intelligent Signal Processing and Communication Systems, pp. 849–853 (1998)

    Google Scholar 

  11. Alparone, L., Baronti, S., Garzelli, A.: A hybrid sigma filter for unbiased and edge-preserving speckle reduction. In: Proceedings of International Geoscience and Remote Sensing Symposium, pp. 1409–1411 (1995)

    Google Scholar 

  12. Lee, J.-S.: Speckle analysis and smoothing of synthetic aperture radar images. Computer Vision, Graphics, Image Processing 17, 24–32 (1981)

    Article  Google Scholar 

  13. Heinonen, P., Neuvo, Y.: FIR-median hybrid filters. IEEE Transactions on Acoustics, Speech, and Signal Processing 35, 832–838 (1987)

    Article  Google Scholar 

  14. Astola, J., Kuosmanen, P.: Fundamentals of nonlinear digital filtering. CRC Press LLC, Boca Raton (1997)

    Google Scholar 

  15. Lukin, V.V., Koivisto, P.T., Ponomarenko, N.N., Abramov, S.K., Astola, J.T.: Two-stage methods for mixed noise removal. In: Proceedings of International Workshop on Nonlinear Signal and Image Processing, pp. 128–133 (2005)

    Google Scholar 

  16. Abramov, S.K., Lukin, V.V., Ponomarenko, N.N., Egiazarian, K., Pogrebnyak, O.B.: Influence of multiplicative noise variance evaluation accuracy on MM-band SLAR image filtering efficiency. In: Proceedings of the Fifth International Kharkov Symposium Physics and Engineering of Millimeter and Sub-Millimeter Waves, vol. 1, pp. 250–252 (2004)

    Google Scholar 

  17. Abramov, S.K., Lukin, V.V., Zelensky, A.A., Astola, J.T.: Blind evaluation of noise variance in images using myriad operation. In: Proceedings of IS&T/SPIE International Conference on Image Processing: Algorithms and Systems, vol. 4667, pp. 192–203 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ponomarenko, N., Lukin, V., Egiazarian, K., Astola, J., Vozel, B., Chehdi, K. (2006). Hybrid Sigma Filter for Processing Images Corrupted by Multiplicative Noise. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_5

Download citation

  • DOI: https://doi.org/10.1007/11864349_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44630-9

  • Online ISBN: 978-3-540-44632-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics