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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFQSB8
Repositorysid.inpe.br/sibgrapi/2017/08.21.19.04
Last Update2017:08.21.19.04.11 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.21.19.04.11
Metadata Last Update2022:06.14.00.08.55 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.59
Citation KeyCosmoInabSall:2017:SiImSu
TitleSingle Image Super-Resolution Using Multiple Extreme Learning Machine Regressors
FormatOn-line
Year2017
Access Date2024, Apr. 29
Number of Files1
Size1609 KiB
2. Context
Author1 Cosmo, Daniel Luis
2 Inaba, Fernando Kentaro
3 Salles, Evandro Ottoni Teatini
Affiliation1 UFES
2 UFES
3 UFES
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 Addressdanielcosmo@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)2017-08-21 19:04:11 :: danielcosmo@gmail.com -> administrator ::
2022-06-14 00:08:55 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsSuper-Resolution
Extreme Learning Machine
AbstractThis paper presents a new technique to solve the single image super resolution reconstruction problem based on multiple extreme learning machine regressors, called here MELM. The MELM employs a feature space of low resolution images, divided in subspaces, and one regressor is trained for each one. In the training task, we employ a color dataset containing 91 images, with approximately 5.3 million pixels, and PSNR and SSIM as metric evaluation. For the experiments we use two datasets, Set 5 and Set 14, to evaluate the results. We observe MELM improves reconstruction quality in about 0.44 dB PSNR in average for Set 5, when compared with a global ELM regressor (GELM), trained for the entire feature space. The proposed method almost reaches deep learning reconstruction quality, without depending on large datasets and long training times, giving a competitive trade off between performance and computational costs.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Single Image Super-Resolution...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Single Image Super-Resolution...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFQSB8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFQSB8
Languageen
Target FilePID4960161.pdf
User Groupdanielcosmo@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 5
sid.inpe.br/sibgrapi/2022/06.10.21.49 3
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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