Please use this identifier to cite or link to this item:
https://olympias.lib.uoi.gr/jspui/handle/123456789/10973Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chantas, G. | en |
| dc.contributor.author | Galatsanos, N. | en |
| dc.contributor.author | Likas, A. | en |
| dc.contributor.author | Saunders, M. | en |
| dc.date.accessioned | 2015-11-24T17:01:45Z | - |
| dc.date.available | 2015-11-24T17:01:45Z | - |
| dc.identifier.issn | 1057-7149 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10973 | - |
| dc.rights | Default Licence | - |
| dc.subject | constrained variational inference | en |
| dc.subject | image restoration | en |
| dc.subject | product prior | en |
| dc.subject | student's-t prior | en |
| dc.subject | variational bayesian inference | en |
| dc.subject | linear-equations | en |
| dc.subject | reconstruction | en |
| dc.subject | parameter | en |
| dc.title | Variational Bayesian image restoration based on a product of t-distributions image prior | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | Doi 10.1109/Tip.2008.2002828 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2008 | - |
| heal.abstract | Image priors based on products have been recognized to offer many advantages because they allow simultaneous enforcement of multiple constraints. However, they are inconvenient for Bayesian inference because it is hard to find their normalization constant in closed form. In this paper, a new Bayesian algorithm is proposed for the image restoration problem that bypasses this difficulty. An image prior is defined by imposing Student-t densities on the outputs of local convolutional filters. A variational methodology, with a constrained expectation step, is used to infer the restored image. Numerical experiments are shown that compare this methodology to previous ones and demonstrate its advantages. | en |
| heal.journalName | Ieee Transactions on Image 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-2008-Variational Bayesian image restoration based on a product of t-distributions image prior.pdf | 1.32 MB | Adobe PDF | View/Open Request a copy |
This item is licensed under a Creative Commons License
