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

  • Tuan-Anh Nguyen
  • Min-Cheol Hong
Part of the Lecture Notes in Computer Science book series (LNCS, 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 

References

  1. 1.
    Arce, G.R.: Nonlinear signal processing: A Statistical approach. John Wiley and Sons Inc. (2004)Google Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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. 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. 7.
    Rank, K., Lendl, M., Unbehauen, R.: Estimation of image noise variance. IEEE Proc. Vision Image Signal Process. 146, 8–84 (1999)CrossRefGoogle Scholar
  8. 8.
    Lee, J.S.: Refined filtering of image noise using local statistics. Computer Vision, Graphics and Image processing 15, 380–389 (1989)CrossRefGoogle Scholar
  9. 9.
    Mastin, G.A.: Adaptive filters for Digital noise smoothing, An evaluation. Computer vision, Graphics and Image processing 31, 103–121 (1985)CrossRefGoogle Scholar
  10. 10.
    Crnojevic, V., Senk, V., Trpovski, Z.: Advanced impulse detection based on pixel-wise MAD. IEEE Signal Process. Letters 11(7), 589–592 (2004)CrossRefGoogle Scholar
  11. 11.
    Aizenberg, I., Butakoff, C.: Effective impulse detector based on rank-order criteria. IEEE Signal Process. Letters 11(3), 363–366 (2004)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 13.
    Elad, M.: On the origin of the bilateral filter and ways to improve it. IEEE Trans. Image Process. 11(10), 1141–1151 (2002)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Processing Letters 9(3), 81–84 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tuan-Anh Nguyen
    • 1
  • Min-Cheol Hong
    • 1
  1. 1.Video and Processing Laboratory, Information and Telecommunication DepartmentSoongsil UniversityDongjak-GuKorea

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