Skip to main content
Springer Nature Link
Log in

A Novel Approach for Bayesian Image Denoising Using a SGLI Prior

  • Conference paper
Advances in Multimedia Information Processing - PCM 2009 (PCM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5879))

Included in the following conference series:

Abstract

This paper provides an effective prior for image denoising in a Bayesian framework. The prior combines two well-known discontinuity measures which have been used in illumination normalization methods. We make use of the two measures as a singular new prior for image denoising in a Bayesian framework. Various experiments show that the proposed prior can reduce noise from corrupted images while preserve edge components efficiently. By comparative studies with conventional methods, we demonstrate that the proposed method achieves impressive performance with respect to noise reduction.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Geman, S., Geman, D.: Stochastic relaxation, Gibbs Distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741 (1984)

    Article  MATH  Google Scholar 

  2. Sanches, J.M., Nascimento, J.C., Marques, J.S.: Medical Image Noise Reduction Using the Sylvester–Lyapunov Equation. IEEE Transactions on Image Processing 17(9), 1522–1539 (2008)

    Article  Google Scholar 

  3. Malfait, M., Roose, D.: Wavelet-based image denoising using a Markov random field a priori model. IEEE Transactions on image processing 6(4), 549–565 (1997)

    Article  Google Scholar 

  4. Sanches, J.M., Marques, J.S.: Image Denoising Using the Lyapunov Equation from Non-uniform Samples. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2006. LNCS, vol. 4141, pp. 351–358. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Li, S.: Markov Random Field Modeling in Computer Vision. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 361–370. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  6. Park, Y.K., Jung, K., Oh, Y., Lee, S., Kim, J.K., Lee, G., Lee, H., Yun, K., Hur, N., Kim, J.: Depth-image-based rendering for 3DTV service over T-DMB. Signal Processing: Image Communication 24(1-2), 122–136 (2009)

    Article  Google Scholar 

  7. Besag, J.: On the statistical analysis of dirty pictures. J. Royal Statistical Soc. 48(3), 259–302 (1986)

    MATH  MathSciNet  Google Scholar 

  8. Kim, H.S., Jung, C., Choi, S., Lee, S., Kim, J.K.: A New Approach for Bayesian Denoising in Images Using an Object Homogeneity Prior. In: Proc. ITC-CSCC, pp. 484–487 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Sungkyunkwan University, Suwon, 440-746, Republic of Korea

    H. Shawn Kim, Cheolkon Jung, Sunghyun Choi, Sangseop Lee & Joong Kyu Kim

Authors
  1. H. Shawn Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheolkon Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sunghyun Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sangseop Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joong Kyu Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Naresuan University, 65000, Phisanulok, Thailand

    Paisarn Muneesawang

  2. Microsoft Research Asia, 100109, Beijing, China

    Feng Wu

  3. Tokyo Institute of Technology, 226-8503, Yokohama, Japan

    Itsuo Kumazawa

  4. Mahanakorn University of Technology, 10530, Bankok, Thailand

    Athikom Roeksabutr

  5. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Mark Liao

  6. Chinese University of Hong Kong, Shatin, N.T., Hong Kong,  

    Xiaoou Tang

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kim, H.S., Jung, C., Choi, S., Lee, S., Kim, J.K. (2009). A Novel Approach for Bayesian Image Denoising Using a SGLI Prior. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds) Advances in Multimedia Information Processing - PCM 2009. PCM 2009. Lecture Notes in Computer Science, vol 5879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10467-1_93

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-10467-1_93

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10466-4

  • Online ISBN: 978-3-642-10467-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics