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Reference TypeConference Proceedings
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyCosmoInabSall:2017:SiImSu
TitleSingle Image Super-Resolution Using Multiple Extreme Learning Machine Regressors
DateOct. 17-20, 2017
Access Date2020, Dec. 04
Number of Files1
Size1609 KiB
Context area
Author1 Cosmo, Daniel Luis
2 Inaba, Fernando Kentaro
3 Salles, Evandro Ottoni Teatini
Affiliation1 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
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2017-08-21 19:04:11 :: -> administrator ::
2020-02-19 02:01:31 :: administrator -> :: 2017
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Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Content TypeExternal Contribution
Tertiary TypeFull Paper
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.
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