Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PFQSB8 |
Repository | sid.inpe.br/sibgrapi/2017/08.21.19.04 |
Last Update | 2017:08.21.19.04.11 administrator |
Metadata | sid.inpe.br/sibgrapi/2017/08.21.19.04.11 |
Metadata Last Update | 2020:02.19.02.01.31 administrator |
Citation Key | CosmoInabSall:2017:SiImSu |
Title | Single Image Super-Resolution Using Multiple Extreme Learning Machine Regressors  |
Format | On-line |
Year | 2017 |
Access Date | 2021, Jan. 26 |
Number of Files | 1 |
Size | 1609 KiB |
Context area | |
Author | 1 Cosmo, Daniel Luis 2 Inaba, Fernando Kentaro 3 Salles, Evandro Ottoni Teatini |
Affiliation | 1 UFES 2 UFES 3 UFES |
Editor | Torchelsen, 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 Address | danielcosmo@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ |
Date | Oct. 17-20, 2017 |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2017-08-21 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 Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | Super-Resolution, Extreme Learning Machine. |
Abstract | This 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 Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PFQSB8 |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PFQSB8 |
Language | en |
Target File | PID4960161.pdf |
User Group | danielcosmo@gmail.com |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PJT9LS 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber 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 |
| |