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https://olympias.lib.uoi.gr/jspui/handle/123456789/10807Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Likas, A. C. | en |
| dc.contributor.author | Galatsanos, N. P. | en |
| dc.date.accessioned | 2015-11-24T17:00:45Z | - |
| dc.date.available | 2015-11-24T17:00:45Z | - |
| dc.identifier.issn | 1053-587X | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10807 | - |
| dc.rights | Default Licence | - |
| dc.subject | bayesian parameter estimation | en |
| dc.subject | blind deconvolution | en |
| dc.subject | graphical models | en |
| dc.subject | image restoration | en |
| dc.subject | variational methods | en |
| dc.subject | expectation-maximization algorithm | en |
| dc.subject | partially known blurs | en |
| dc.subject | maximum-likelihood | en |
| dc.subject | em algorithm | en |
| dc.subject | restoration | en |
| dc.subject | identification | en |
| dc.subject | regularization | en |
| dc.title | A variational approach for Bayesian blind image deconvolution | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | Doi 10.1109/Tsp.2004.831119 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2004 | - |
| heal.abstract | In this paper, the blind image deconvolution (BID) problem is addressed using the Bayesian framework. In order to solve for the proposed Bayesian model, we present a new methodology based on a variational approximation, which has been recently introduced for several machine learning problems, and can be viewed as a generalization of the expectation maximization (EM) algorithm. This methodology reaps all the benefits of a "full Bayesian model" while bypassing some of its difficulties. We present three algorithms that solve the proposed Bayesian problem in closed form and can be implemented in the discrete Fourier domain. This makes them very cost effective even for very large images. We demonstrate with numerical experiments that these algorithms yield promising improvements as compared to previous BID algorithms. Furthermore, the proposed methodology is quite general with potential application to other Bayesian models for this and other imaging problems. | en |
| heal.journalName | Ieee Transactions on Signal Processing | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Likas-2004-A variational approach for Bayesian blind image deconvolution.pdf | 651.94 kB | Adobe PDF | View/Open Request a copy |
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