Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

Pacific-Rim Symposium on Image and Video Technology

PSIVT 2011: Advances in Image and Video Technology pp 157–167Cite as

  1. Home
  2. Advances in Image and Video Technology
  3. Conference paper
Filtering-Based Noise Estimation for Denoising the Image Degraded by Gaussian Noise

Filtering-Based Noise Estimation for Denoising the Image Degraded by Gaussian Noise

  • Tuan-Anh Nguyen17 &
  • Min-Cheol Hong17 
  • Conference paper
  • 1594 Accesses

  • 4 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7088)

Abstract

In this paper, a denoising algorithm for the Gaussian noise image using filtering-based estimation is presented. To adaptively deal with variety of the amount of noise corruption, the algorithm initially estimates the noise density from the degraded image. The standard deviation of the noise is computed from the different images between the noisy input and its’ pre-filtered version. In addition, the modified Gaussian noise removal filter based on the local statistics such as local weighted mean, local weighted activity and local maximum is flexibly used to control the degree of noise suppression. Experimental results show the superior performance of the proposed filter algorithm compared to the other standard algorithms in terms of both subjective and objective evaluations.

Keywords

  • Local statistics
  • Gaussian filtering
  • noise estimation
  • Denoising
  • Gaussian noise

Download conference paper PDF

References

  1. Arce, G.R.: Nonlinear signal processing: A Statistical approach. John Wiley and Sons Inc. (2004)

    Google Scholar 

  2. Nodes, T.A., Gallagher, N.C.: Median filters: some modifications and their properties. IEEE Trans. Acoustics, Speech and Signal process. 30(5), 739–746 (1982)

    CrossRef  Google Scholar 

  3. Bednar, J.B., Watt, T.K.: Alpha-trimmed means and their relationship to median filter. IEEE Trans. Acoustics, Speech and Signal Process. 32(1), 145–153 (1984)

    CrossRef  Google Scholar 

  4. Olsen, S.I.: Noise Variance Estimation in Images: An evaluation, Computer Vision Graphics Image Processing. Graphic Models and Image Processing 55(4), 319–323 (1993)

    CrossRef  Google Scholar 

  5. Lee, J.S., Hoppel, K.: Noise modeling and estimation of remotely-sensed image. In: International Conference on Geoscience and Remote Sensing, Vancouver, Canada, vol. 2, pp. 1005–1008 (1989)

    Google Scholar 

  6. Shin, D.H., Park, R.H., Yang, S.J.: Block-based noise estimation using adaptive Gaussian filtering. IEEE Trans. on Consumer Electronics 51(1) (2005)

    Google Scholar 

  7. Rank, K., Lendl, M., Unbehauen, R.: Estimation of image noise variance. IEEE Proc. Vision Image Signal Process. 146, 8–84 (1999)

    CrossRef  Google Scholar 

  8. Lee, J.S.: Refined filtering of image noise using local statistics. Computer Vision, Graphics and Image processing 15, 380–389 (1989)

    CrossRef  Google Scholar 

  9. Mastin, G.A.: Adaptive filters for Digital noise smoothing, An evaluation. Computer vision, Graphics and Image processing 31, 103–121 (1985)

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  11. Aizenberg, I., Butakoff, C.: Effective impulse detector based on rank-order criteria. IEEE Signal Process. Letters 11(3), 363–366 (2004)

    CrossRef  Google Scholar 

  12. Zhang, X., Xiong, Y.: Impulse noise removal using directional differences based noise detector and adaptive weighted mean filter. IEEE Signal Process. Letters 16(4), 295–298 (2009)

    CrossRef  Google Scholar 

  13. Elad, M.: On the origin of the bilateral filter and ways to improve it. IEEE Trans. Image Process. 11(10), 1141–1151 (2002)

    CrossRef  MathSciNet  Google Scholar 

  14. Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Processing Letters 9(3), 81–84 (2002)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Video and Processing Laboratory, Information and Telecommunication Department, Soongsil University, 156-743 Sangdo-Dong, Dongjak-Gu, Seoul, Korea

    Tuan-Anh Nguyen & Min-Cheol Hong

Authors
  1. Tuan-Anh Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min-Cheol Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Gwangju Institute of Science and Technology (GIST), 1 Oryong-dong Buk-gu, 500-712, Gwangju, South Korea

    Yo-Sung Ho

Rights and permissions

Reprints and Permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nguyen, TA., Hong, MC. (2011). Filtering-Based Noise Estimation for Denoising the Image Degraded by Gaussian Noise. In: Ho, YS. (eds) Advances in Image and Video Technology. PSIVT 2011. Lecture Notes in Computer Science, vol 7088. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25346-1_15

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-25346-1_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25345-4

  • Online ISBN: 978-3-642-25346-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • The International Association for Pattern Recognition

    Published in cooperation with

    http://www.iapr.org/

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature