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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PF65BB
Repositorysid.inpe.br/sibgrapi/2017/08.17.12.24
Last Update2017:08.17.12.24.04 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.17.12.24.04
Metadata Last Update2022:06.14.00.08.44 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.58
Citation KeyPiresSaPeSoLePa:2017:RoReBo
TitleA Robust Restricted Boltzmann Machine for Binary Image Denoising
FormatOn-line
Year2017
Access Date2025, Mar. 21
Number of Files1
Size737 KiB
2. Context
Author1 Pires, Rafael Gonçalves
2 Santos, Daniel Felipe Silva
3 Pereira, Luís Augusto Martins
4 Souza, Gustavo Botelho de
5 Levada, Alexandre Luis Magalhães
6 Papa, João Paulo
Affiliation1 Department of Computing Federal University of São Carlos São Carlos - SP, Brazil
2 Department of Computing São Paulo State University Bauru - SP, Brazil
3 Institute of Computing University of Campinas Campinas - SP, Brazil
4 Department of Computing Federal University of São Carlos São Carlos - SP, Brazil
5 Department of Computing Federal University of São Carlos São Carlos - SP, Brazil
6 Department of Computing São Paulo State University Bauru - SP, Brazil
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressrafapires@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-17 12:24:04 :: rafapires@gmail.com -> administrator ::
2022-06-14 00:08:44 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsimage restoration
machine learning
restricted boltzmann machines
AbstractDuring the image acquisition process, some level of noise is usually added to the real data mainly due to physical limitations of the acquisition sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be processed in order to attenuate its noise without loosing details. Machine learning approaches have been successfully used for image denoising. Among such approaches, Restricted Boltzmann Machine (RBM) is one of the most used technique for this purpose. Here, we propose to enhance the RBM performance on image denoising by adding a posterior supervision before its final denoising step. To this purpose, we propose a simple but effective approach that performs a fine-tuning in the RBM model. Experiments on public datasets corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach with respect to some state-of-the-art image denoising approaches.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > A Robust Restricted...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > A Robust Restricted...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 17/08/2017 09:24 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PF65BB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PF65BB
Languageen
Target FilePID4955441.pdf
User Grouprafapires@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 33
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark 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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