Image Noises Removal on Alpha-Stable via Bayesian Estimator

Xu Huang, A Madoc

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    Abstract

    A maximum likelihood Bayesian estimator that recovers the signal component of the wavelet coefficients from original images by using an /spl alpha/-stable signal prior distribution is discussed. As we discussed in our earlier paper that the Bayesian estimator can approximate impulsive noise more accurately than other models and that the general case of the Bayesian processor does not have a closed-form expression. The attentions drawn by this paper is the behaviours of /spl alpha/ /spl isin/ (0.1] following we discussed /spl alpha/ /spl isin/ [D. L. Donoho and I. M. Juhnstone, 1995] [D. L. Donoho, May 1995] in our earlier paper [X. Huang et al., 2002]. Closer to a realistic situation, and unlike conventional methods used for Bayesian estimator, for the case discussed here it is not necessary to know the variance of the noise. The parameters relative to Bayesian estimators of the model built up are carefully investigated after an investigation of /spl alpha/-stable simulations for a maximum likelihood estimator. As an example, an improved Bayesian estimator that is a natural extension of the Wiener solution and other wavelet denoising (soft and hard threshold methods), is presented for illustration purposes
    Original languageEnglish
    Title of host publicationThe Seventh International Symposium on Signal Processing and its Applications
    EditorsSuviSoft Oy
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1-4
    Number of pages4
    ISBN (Print)9780780379466
    DOIs
    Publication statusPublished - 2003
    EventISSPA 2003 - Paris, France
    Duration: 1 Jul 20034 Jul 2003

    Conference

    ConferenceISSPA 2003
    CountryFrance
    CityParis
    Period1/07/034/07/03

    Fingerprint

    Bayesian Estimator
    Noise Removal
    Maximum Likelihood Estimator
    Wavelet Denoising
    Impulsive Noise
    Wavelet Coefficients
    Natural Extension
    Prior distribution
    Closed-form
    Necessary
    Model
    Simulation

    Cite this

    Huang, X., & Madoc, A. (2003). Image Noises Removal on Alpha-Stable via Bayesian Estimator. In S. Oy (Ed.), The Seventh International Symposium on Signal Processing and its Applications (pp. 1-4). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISSPA.2003.1224841
    Huang, Xu ; Madoc, A. / Image Noises Removal on Alpha-Stable via Bayesian Estimator. The Seventh International Symposium on Signal Processing and its Applications. editor / SuviSoft Oy. IEEE, Institute of Electrical and Electronics Engineers, 2003. pp. 1-4
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    title = "Image Noises Removal on Alpha-Stable via Bayesian Estimator",
    abstract = "A maximum likelihood Bayesian estimator that recovers the signal component of the wavelet coefficients from original images by using an /spl alpha/-stable signal prior distribution is discussed. As we discussed in our earlier paper that the Bayesian estimator can approximate impulsive noise more accurately than other models and that the general case of the Bayesian processor does not have a closed-form expression. The attentions drawn by this paper is the behaviours of /spl alpha/ /spl isin/ (0.1] following we discussed /spl alpha/ /spl isin/ [D. L. Donoho and I. M. Juhnstone, 1995] [D. L. Donoho, May 1995] in our earlier paper [X. Huang et al., 2002]. Closer to a realistic situation, and unlike conventional methods used for Bayesian estimator, for the case discussed here it is not necessary to know the variance of the noise. The parameters relative to Bayesian estimators of the model built up are carefully investigated after an investigation of /spl alpha/-stable simulations for a maximum likelihood estimator. As an example, an improved Bayesian estimator that is a natural extension of the Wiener solution and other wavelet denoising (soft and hard threshold methods), is presented for illustration purposes",
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    language = "English",
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    booktitle = "The Seventh International Symposium on Signal Processing and its Applications",
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    Huang, X & Madoc, A 2003, Image Noises Removal on Alpha-Stable via Bayesian Estimator. in S Oy (ed.), The Seventh International Symposium on Signal Processing and its Applications. IEEE, Institute of Electrical and Electronics Engineers, pp. 1-4, ISSPA 2003, Paris, France, 1/07/03. https://doi.org/10.1109/ISSPA.2003.1224841

    Image Noises Removal on Alpha-Stable via Bayesian Estimator. / Huang, Xu; Madoc, A.

    The Seventh International Symposium on Signal Processing and its Applications. ed. / SuviSoft Oy. IEEE, Institute of Electrical and Electronics Engineers, 2003. p. 1-4.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    AU - Madoc, A

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    N2 - A maximum likelihood Bayesian estimator that recovers the signal component of the wavelet coefficients from original images by using an /spl alpha/-stable signal prior distribution is discussed. As we discussed in our earlier paper that the Bayesian estimator can approximate impulsive noise more accurately than other models and that the general case of the Bayesian processor does not have a closed-form expression. The attentions drawn by this paper is the behaviours of /spl alpha/ /spl isin/ (0.1] following we discussed /spl alpha/ /spl isin/ [D. L. Donoho and I. M. Juhnstone, 1995] [D. L. Donoho, May 1995] in our earlier paper [X. Huang et al., 2002]. Closer to a realistic situation, and unlike conventional methods used for Bayesian estimator, for the case discussed here it is not necessary to know the variance of the noise. The parameters relative to Bayesian estimators of the model built up are carefully investigated after an investigation of /spl alpha/-stable simulations for a maximum likelihood estimator. As an example, an improved Bayesian estimator that is a natural extension of the Wiener solution and other wavelet denoising (soft and hard threshold methods), is presented for illustration purposes

    AB - A maximum likelihood Bayesian estimator that recovers the signal component of the wavelet coefficients from original images by using an /spl alpha/-stable signal prior distribution is discussed. As we discussed in our earlier paper that the Bayesian estimator can approximate impulsive noise more accurately than other models and that the general case of the Bayesian processor does not have a closed-form expression. The attentions drawn by this paper is the behaviours of /spl alpha/ /spl isin/ (0.1] following we discussed /spl alpha/ /spl isin/ [D. L. Donoho and I. M. Juhnstone, 1995] [D. L. Donoho, May 1995] in our earlier paper [X. Huang et al., 2002]. Closer to a realistic situation, and unlike conventional methods used for Bayesian estimator, for the case discussed here it is not necessary to know the variance of the noise. The parameters relative to Bayesian estimators of the model built up are carefully investigated after an investigation of /spl alpha/-stable simulations for a maximum likelihood estimator. As an example, an improved Bayesian estimator that is a natural extension of the Wiener solution and other wavelet denoising (soft and hard threshold methods), is presented for illustration purposes

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    Huang X, Madoc A. Image Noises Removal on Alpha-Stable via Bayesian Estimator. In Oy S, editor, The Seventh International Symposium on Signal Processing and its Applications. IEEE, Institute of Electrical and Electronics Engineers. 2003. p. 1-4 https://doi.org/10.1109/ISSPA.2003.1224841