Noise Removal for Images by Wavelet-Based Bayesian Estimator via Levy Process Analysis

Xu Huang, A Madoc, Michael Wagner

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

    1 Citation (Scopus)

    Abstract

    There are many noise sources for images. Images are, in many cases, degraded even before they are encoded. Previously, we focused on Poisson noise (Huang, X. et al., IEEE Int. Conf. on Multimedia and Expo, vol.1, p.593, 2003). 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 a Bayesian estimator that recovers the signal component of the wavelet coefficients in the original images by using an alpha-stable signal prior distribution is demonstrated for Poisson noise removal. The paper extends, via Levy process analysis, our previous results to more complex cases of noise comprised of compound Poisson and Gaussian. As an example, an improved Bayesian estimator that is a natural extension of other wavelet denoising (soft and hard threshold methods) via a colour image is presented to illustrate our discussion; even though computers did not know the noise, this method works well.
    Original languageEnglish
    Title of host publicationIEEE International Conference on Multimedia Expo (ICME 2004)
    EditorsWilliam Grosby, Liam-Gee Chen
    Place of PublicationTaipei, Taiwan.
    PublisherIEEE
    Pages1-4
    Number of pages4
    ISBN (Print)0780386035
    DOIs
    Publication statusPublished - 2004
    EventInternational Conference on Multimedia and Expo (ICME 04) - Taipei, Taiwan, Province of China
    Duration: 27 Jun 200430 Jun 2004

    Conference

    ConferenceInternational Conference on Multimedia and Expo (ICME 04)
    CountryTaiwan, Province of China
    CityTaipei
    Period27/06/0430/06/04

    Fingerprint

    estimators
    multimedia
    random noise
    color
    thresholds
    coefficients

    Cite this

    Huang, X., Madoc, A., & Wagner, M. (2004). Noise Removal for Images by Wavelet-Based Bayesian Estimator via Levy Process Analysis. In W. Grosby, & L-G. Chen (Eds.), IEEE International Conference on Multimedia Expo (ICME 2004) (pp. 1-4). Taipei, Taiwan.: IEEE. https://doi.org/10.1109/ICME.2004.1394195
    Huang, Xu ; Madoc, A ; Wagner, Michael. / Noise Removal for Images by Wavelet-Based Bayesian Estimator via Levy Process Analysis. IEEE International Conference on Multimedia Expo (ICME 2004). editor / William Grosby ; Liam-Gee Chen. Taipei, Taiwan. : IEEE, 2004. pp. 1-4
    @inproceedings{e94b9096979c4244ae049ffe6c762d5a,
    title = "Noise Removal for Images by Wavelet-Based Bayesian Estimator via Levy Process Analysis",
    abstract = "There are many noise sources for images. Images are, in many cases, degraded even before they are encoded. Previously, we focused on Poisson noise (Huang, X. et al., IEEE Int. Conf. on Multimedia and Expo, vol.1, p.593, 2003). 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 a Bayesian estimator that recovers the signal component of the wavelet coefficients in the original images by using an alpha-stable signal prior distribution is demonstrated for Poisson noise removal. The paper extends, via Levy process analysis, our previous results to more complex cases of noise comprised of compound Poisson and Gaussian. As an example, an improved Bayesian estimator that is a natural extension of other wavelet denoising (soft and hard threshold methods) via a colour image is presented to illustrate our discussion; even though computers did not know the noise, this method works well.",
    author = "Xu Huang and A Madoc and Michael Wagner",
    year = "2004",
    doi = "10.1109/ICME.2004.1394195",
    language = "English",
    isbn = "0780386035",
    pages = "1--4",
    editor = "William Grosby and Liam-Gee Chen",
    booktitle = "IEEE International Conference on Multimedia Expo (ICME 2004)",
    publisher = "IEEE",

    }

    Huang, X, Madoc, A & Wagner, M 2004, Noise Removal for Images by Wavelet-Based Bayesian Estimator via Levy Process Analysis. in W Grosby & L-G Chen (eds), IEEE International Conference on Multimedia Expo (ICME 2004). IEEE, Taipei, Taiwan., pp. 1-4, International Conference on Multimedia and Expo (ICME 04), Taipei, Taiwan, Province of China, 27/06/04. https://doi.org/10.1109/ICME.2004.1394195

    Noise Removal for Images by Wavelet-Based Bayesian Estimator via Levy Process Analysis. / Huang, Xu; Madoc, A; Wagner, Michael.

    IEEE International Conference on Multimedia Expo (ICME 2004). ed. / William Grosby; Liam-Gee Chen. Taipei, Taiwan. : IEEE, 2004. p. 1-4.

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

    TY - GEN

    T1 - Noise Removal for Images by Wavelet-Based Bayesian Estimator via Levy Process Analysis

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    AU - Madoc, A

    AU - Wagner, Michael

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    N2 - There are many noise sources for images. Images are, in many cases, degraded even before they are encoded. Previously, we focused on Poisson noise (Huang, X. et al., IEEE Int. Conf. on Multimedia and Expo, vol.1, p.593, 2003). 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 a Bayesian estimator that recovers the signal component of the wavelet coefficients in the original images by using an alpha-stable signal prior distribution is demonstrated for Poisson noise removal. The paper extends, via Levy process analysis, our previous results to more complex cases of noise comprised of compound Poisson and Gaussian. As an example, an improved Bayesian estimator that is a natural extension of other wavelet denoising (soft and hard threshold methods) via a colour image is presented to illustrate our discussion; even though computers did not know the noise, this method works well.

    AB - There are many noise sources for images. Images are, in many cases, degraded even before they are encoded. Previously, we focused on Poisson noise (Huang, X. et al., IEEE Int. Conf. on Multimedia and Expo, vol.1, p.593, 2003). 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 a Bayesian estimator that recovers the signal component of the wavelet coefficients in the original images by using an alpha-stable signal prior distribution is demonstrated for Poisson noise removal. The paper extends, via Levy process analysis, our previous results to more complex cases of noise comprised of compound Poisson and Gaussian. As an example, an improved Bayesian estimator that is a natural extension of other wavelet denoising (soft and hard threshold methods) via a colour image is presented to illustrate our discussion; even though computers did not know the noise, this method works well.

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    M3 - Conference contribution

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    Huang X, Madoc A, Wagner M. Noise Removal for Images by Wavelet-Based Bayesian Estimator via Levy Process Analysis. In Grosby W, Chen L-G, editors, IEEE International Conference on Multimedia Expo (ICME 2004). Taipei, Taiwan.: IEEE. 2004. p. 1-4 https://doi.org/10.1109/ICME.2004.1394195