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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW4/35S5N8H
Repositorysid.inpe.br/sibgrapi@80/2009/08.17.17.40
Last Update2009:08.17.17.40.52 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi@80/2009/08.17.17.40.53
Metadata Last Update2022:06.14.00.13.58 (UTC) administrator
DOI10.1109/SIBGRAPI.2009.8
Citation KeyLevadaMascTann:2009:NoItAp
TitleGSAShrink: A Novel Iterative Approach for Wavelet-Based Image Denoising
FormatPrinted, On-line.
Year2009
Access Date2024, May 02
Number of Files1
Size409 KiB
2. Context
Author1 Levada, Alexandre L. M.
2 Mascarenhas, Nelson Delfino d'Ávila
3 Tannús, Alberto
Affiliation1 Universidade de São Paulo
2 Universidade Federal de São Carlos
3 Universidade de São Paulo
EditorNonato, Luis Gustavo
Scharcanski, Jacob
e-Mail Addressalexandre.levada@gmail.com
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 22 (SIBGRAPI)
Conference LocationRio de Janeiro, RJ, Brazil
Date11-14 Oct. 2009
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2010-08-28 20:03:26 :: alexandre.levada@gmail.com -> administrator ::
2022-06-14 00:13:58 :: administrator -> :: 2009
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsImage Denoising
Wavelets
Bayesian Estimation
Maximum a Posteriori
Game Strategy Approach
AbstractIn this paper we propose a novel iterative algorithm for wavelet-based image denoising following a Maximum a Posteriori (MAP) approach. The wavelet shrinkage problem is modeled according to the Bayesian paradigm, providing a strong and extremely flexible framework for solving general image denoising problems. To approximate the MAP estimator, we propose GSAShrink, a modified version of a known combinatorial optimization algorithm based on non-cooperative game theory (Game Strategy Approach, or GSA). In order to modify the original algorithm to our purposes, we generalize GSA by introducing some additional control parameters and steps to reflect the nature of wavelet shrinkage applications. To test and evaluate the proposed method, experiments using several wavelet basis on noisy images are proposed. Additionally to better visual quality, the obtained results produce quantitative metrics (MSE, PSNR, ISNR and UIQ) that show significant improvements in comparison to traditional wavelet denoising approaches known as soft and hard thresholding, indicating the effectiveness of the proposed algorithm.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2009 > GSAShrink: A Novel...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > GSAShrink: A Novel...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW4/35S5N8H
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW4/35S5N8H
Languageen
Target FileGSAShrink_Sibgrapi2009.pdf
User Groupalexandre.levada@gmail.com
Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46SJQ2S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.14.19.43 11
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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