Image Noises Removal on Alpha-Stable via Bayesian Estimator

Xu Huang, A Madoc

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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
Number of pages4
ISBN (Print)9780780379466
Publication statusPublished - 2003
EventISSPA 2003 - Paris, France
Duration: 1 Jul 20034 Jul 2003


ConferenceISSPA 2003


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