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231223s2008 xx ||||| 00| ||eng c |
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|a DE-627
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|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Hammond, David K
|e verfasserin
|4 aut
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| 245 |
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|a Image modeling and denoising with orientation-adapted Gaussian scale mixtures
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|c 2008
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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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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 09.12.2008
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|a Date Revised 25.04.2023
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|a published: Print
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|a Citation Status MEDLINE
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|a We develop a statistical model to describe the spatially varying behavior of local neighborhoods of coefficients in a multiscale image representation. Neighborhoods are modeled as samples of a multivariate Gaussian density that are modulated and rotated according to the values of two hidden random variables, thus allowing the model to adapt to the local amplitude and orientation of the signal. A third hidden variable selects between this oriented process and a nonoriented scale mixture of Gaussians process, thus providing adaptability to the local orientedness of the signal. Based on this model, we develop an optimal Bayesian least squares estimator for denoising images and show through simulations that the resulting method exhibits significant improvement over previously published results obtained with Gaussian scale mixtures
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|a Journal Article
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|a Simoncelli, Eero P
|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 17(2008), 11 vom: 15. Nov., Seite 2089-101
|w (DE-627)NLM09821456X
|x 1057-7149
|7 nnns
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| 773 |
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|g volume:17
|g year:2008
|g number:11
|g day:15
|g month:11
|g pages:2089-101
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