Close
Metadata

Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3PFQSB8
Repositorysid.inpe.br/sibgrapi/2017/08.21.19.04
Last Update2017:08.21.19.04.11 administrator
Metadatasid.inpe.br/sibgrapi/2017/08.21.19.04.11
Metadata Last Update2020:02.19.02.01.31 administrator
Citation KeyCosmoInabSall:2017:SiImSu
TitleSingle Image Super-Resolution Using Multiple Extreme Learning Machine Regressors
FormatOn-line
Year2017
Access Date2021, Jan. 26
Number of Files1
Size1609 KiB
Context area
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
DateOct. 17-20, 2017
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-21 19:04:11 :: danielcosmo@gmail.com -> administrator ::
2020-02-19 02:01:31 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
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.
source Directory Contentthere are no files
agreement Directory Content
agreement.html 21/08/2017 16:04 1.2 KiB 
Conditions of access and use area
data URLhttp://urlib.net/rep/8JMKD3MGPAW/3PFQSB8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFQSB8
Languageen
Target FilePID4960161.pdf
User Groupdanielcosmo@gmail.com
Visibilityshown
Update Permissionnot transferred
Allied materials area
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PJT9LS
8JMKD3MGPAW/3PKCC58
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Notes area
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume

Close