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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Chantas, Giannis K
|e verfasserin
|4 aut
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| 245 |
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|a Bayesian restoration using a new nonstationary edge-preserving image prior
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|c 2006
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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 Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 20.11.2006
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|a Date Revised 26.10.2019
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|a published: Print
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|a Citation Status MEDLINE
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|a In this paper, we propose a class of image restoration algorithms based on the Bayesian approach and a new hierarchical spatially adaptive image prior. The proposed prior has the following two desirable features. First, it models the local image discontinuities in different directions with a model which is continuous valued. Thus, it preserves edges and generalizes the on/off (binary) line process idea used in previous image priors within the context of Markov random fields (MRFs). Second, it is Gaussian in nature and provides estimates that are easy to compute. Using this new hierarchical prior, two restoration algorithms are derived. The first is based on the maximum a posteriori principle and the second on the Bayesian methodology. Numerical experiments are presented that compare the proposed algorithms among themselves and with previous stationary and non stationary MRF-based with line process algorithms. These experiments demonstrate the advantages of the proposed prior
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|a Journal Article
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|a Galatsanos, Nikolaos P
|e verfasserin
|4 aut
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| 700 |
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|a Likas, Aristidis C
|e verfasserin
|4 aut
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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 1992
|g 15(2006), 10 vom: 08. Okt., Seite 2987-97
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g volume:15
|g year:2006
|g number:10
|g day:08
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|g pages:2987-97
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