Bayesian wavelet-based image deconvolution : a GEM algorithm exploiting a class of heavy-tailed priors
Image deconvolution is formulated in the wavelet domain under the Bayesian framework. The well-known sparsity of the wavelet coefficients of real-world images is modeled by heavy-tailed priors belonging to the Gaussian scale mixture (GSM) class; i.e., priors given by a linear (finite of infinite) co...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1997. - 15(2006), 4 vom: 27. Apr., Seite 937-51 |
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| Auteur principal: | |
| Format: | Article |
| Langue: | English |
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2006
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Evaluation Study Journal Article Research Support, Non-U.S. Gov't |