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231223s2011 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2010.2073477
|2 doi
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|a pubmed24n1222.xml
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|a eng
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|a Luisier, Florian
|e verfasserin
|4 aut
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| 245 |
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|a Image denoising in mixed Poisson-Gaussian noise
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|c 2011
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 27.05.2011
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|a Date Revised 13.12.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-Gaussian noise. We express the denoising process as a linear expansion of thresholds (LET) that we optimize by relying on a purely data-adaptive unbiased estimate of the mean-squared error (MSE), derived in a non-Bayesian framework (PURE: Poisson-Gaussian unbiased risk estimate). We provide a practical approximation of this theoretical MSE estimate for the tractable optimization of arbitrary transform-domain thresholding. We then propose a pointwise estimator for undecimated filterbank transforms, which consists of subband-adaptive thresholding functions with signal-dependent thresholds that are globally optimized in the image domain. We finally demonstrate the potential of the proposed approach through extensive comparisons with state-of-the-art techniques that are specifically tailored to the estimation of Poisson intensities. We also present denoising results obtained on real images of low-count fluorescence microscopy
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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1 |
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|a Blu, Thierry
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Unser, Michael
|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 20(2011), 3 vom: 14. März, Seite 696-708
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|x 1941-0042
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|g volume:20
|g year:2011
|g number:3
|g day:14
|g month:03
|g pages:696-708
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|u http://dx.doi.org/10.1109/TIP.2010.2073477
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