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231224s2013 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2012.2220150
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|a eng
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|a Ho, Jinn
|e verfasserin
|4 aut
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|a Wavelet Bayesian network image denoising
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|c 2013
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|a Text
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|a ƒaComputermedien
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|a Date Completed 22.07.2013
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|a Date Revised 08.02.2013
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a From the perspective of the Bayesian approach, the denoising problem is essentially a prior probability modeling and estimation task. In this paper, we propose an approach that exploits a hidden Bayesian network, constructed from wavelet coefficients, to model the prior probability of the original image. Then, we use the belief propagation (BP) algorithm, which estimates a coefficient based on all the coefficients of an image, as the maximum-a-posterior (MAP) estimator to derive the denoised wavelet coefficients. We show that if the network is a spanning tree, the standard BP algorithm can perform MAP estimation efficiently. Our experiment results demonstrate that, in terms of the peak-signal-to-noise-ratio and perceptual quality, the proposed approach outperforms state-of-the-art algorithms on several images, particularly in the textured regions, with various amounts of white Gaussian noise
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Hwang, Wen-Liang
|e verfasserin
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|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 22(2013), 4 vom: 21. Apr., Seite 1277-90
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