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Image Multi-noise Removal via Lévy Process Analysis

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3684))

Abstract

Almost every digital image is unavoidably contaminated by various noise sources. In our previous paper, we focused on Gaussian and Poisson noises. Unlike additive Gaussian noise, Poisson noise is signal-dependent and separating signal from noise is a difficult task. A wavelet-based maximum likelihood method for Bayesian estimator that recovers the signal component of the wavelet coefficients in original images by using an alpha-stable signal prior distribution is demonstrated to the discussed noise removal. Current paper is to extend out previous results to more complex cases that noises comprised of compound Poisson, Gaussian, and impulse noises via Lévy process analysis. As an example, an improved Bayesian estimator that is a natural extension of other wavelet denoising via a colour image is presented to illustrate our discussion.

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Author information

Authors and Affiliations

  1. School of Information Sciences and Engineering, University of Canberra, ACT, 2601, Australia

    Xu Huang

  2. Center for Actuarial Studies, Department of Economics, University of Melbourne, VIC 3051, Australia

    A. C. Madoc

Authors
  1. Xu Huang
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  2. A. C. Madoc
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Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, Moulsecoomb, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Huang, X., Madoc, A.C. (2005). Image Multi-noise Removal via Lévy Process Analysis. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_4

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  • DOI: https://doi.org/10.1007/11554028_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28897-8

  • Online ISBN: 978-3-540-31997-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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