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 contributionpeer-review

1 Citation (Scopus)
18 Downloads (Pure)


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.
Number of pages4
ISBN (Print)0780386035
Publication statusPublished - 2004
EventInternational Conference on Multimedia and Expo (ICME 04) - Taipei, Taiwan, Province of China
Duration: 27 Jun 200430 Jun 2004


ConferenceInternational Conference on Multimedia and Expo (ICME 04)
Country/TerritoryTaiwan, Province of China


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