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|a eng
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|a Bioucas-Dias, José M
|e verfasserin
|4 aut
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|a Bayesian wavelet-based image deconvolution
|b a GEM algorithm exploiting a class of heavy-tailed priors
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|c 2006
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Date Completed 10.05.2006
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|a Date Revised 10.12.2019
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|a published: Print
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|a Citation Status MEDLINE
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|a 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) combination of Gaussian densities. This class includes, among others, the generalized Gaussian, the Jeffreys, and the Gaussian mixture priors. Necessary and sufficient conditions are stated under which the prior induced by a thresholding/shrinking denoising rule is a GSM. This result is then used to show that the prior induced by the "nonnegative garrote" thresholding/shrinking rule, herein termed the garrote prior, is a GSM. To compute the maximum a posteriori estimate, we propose a new generalized expectation maximization (GEM) algorithm, where the missing variables are the scale factors of the GSM densities. The maximization step of the underlying expectation maximization algorithm is replaced with a linear stationary second-order iterative method. The result is a GEM algorithm of O(N log N) computational complexity. In a series of benchmark tests, the proposed approach outperforms or performs similarly to state-of-the art methods, demanding comparable (in some cases, much less) computational complexity
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|a Evaluation Study
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1997
|g 15(2006), 4 vom: 27. Apr., Seite 937-51
|w (DE-627)NLM09821456X
|x 1057-7149
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|g volume:15
|g year:2006
|g number:4
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|g month:04
|g pages:937-51
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