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2011
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Bayesian Deblurring with Integrated Noise Estimation

8 years 1 months ago
Bayesian Deblurring with Integrated Noise Estimation
Conventional non-blind image deblurring algorithms involve natural image priors and maximum a-posteriori (MAP) estimation. As a consequence of MAP estimation, separate pre-processing steps such as noise estimation and training of the regularization parameter are necessary to avoid user interaction. Moreover, MAP estimates involving standard natural image priors have been found lacking in terms of restoration performance. To address these issues we introduce an integrated Bayesian framework that unifies non-blind deblurring and noise estimation, thus freeing the user of tediously pre-determining a noise level. A samplingbased technique allows to integrate out the unknown noise level and to perform deblurring using the Bayesian minimum mean squared error estimate (MMSE), which requires no regularization parameter and yields higher performance than MAP estimates when combined with a learned highorder image prior. A quantitative evaluation demonstrates state-of-the-art results for both n...
Uwe Schmidt, Kevin Schelten, Stefan Roth
Added 30 Apr 2011
Updated 30 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Uwe Schmidt, Kevin Schelten, Stefan Roth
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