Exact Distribution of Edge-Preserving MAP Estimators for Linear Signal Models with Gaussian Measurement Noise
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.contributor.author | Erdogan, Hakan | en_US |
dc.contributor.author | Wu, Wei Biao | en_US |
dc.date.accessioned | 2011-08-18T18:21:20Z | |
dc.date.available | 2011-08-18T18:21:20Z | |
dc.date.issued | 2000-06 | en_US |
dc.identifier.citation | Fessler, J. A.; Erdogan, H.; Wu, W. B. (2000). "Exact Distribution of Edge-Preserving MAP Estimators for Linear Signal Models with Gaussian Measurement Noise." IEEE Transactions on Image Processing 9(6): 1049-1055. <http://hdl.handle.net/2027.42/85999> | en_US |
dc.identifier.issn | 1057-7149 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/85999 | |
dc.description.abstract | We derive the exact statistical distribution of maximum a posteriori (MAP) estimators having edge-preserving nonGaussian priors. Such estimators have been widely advocated for image restoration and reconstruction problems. Previous investigations of these image recovery methods have been primarily empirical; the distribution we derive enables theoretical analysis. The signal model is linear with Gaussian measurement noise. We assume that the energy function of the prior distribution is chosen to ensure a unimodal posterior distribution (for which convexity of the energy function is sufficient), and that the energy function satisfies a uniform Lipschitz regularity condition. The regularity conditions are sufficiently general to encompass popular priors such as the generalized Gaussian Markov random field prior and the Huber prior, even though those priors are not everywhere twice continuously differentiable. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Exact Distribution of Edge-Preserving MAP Estimators for Linear Signal Models with Gaussian Measurement Noise | en_US |
dc.type | article | en_US |
dc.subject.hlbsecondlevel | Biomedical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Statistics. | en_US |
dc.contributor.affiliationother | IBM T.J. Watson Research Labs, Yorktown Heights, NY 10598 USA. | en_US |
dc.identifier.pmid | 18255475 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85999/1/Fessler81.pdf | |
dc.identifier.doi | 10.1109/83.846247 | en_US |
dc.identifier.source | IEEE Transactions on Image Processing | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.