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		<identifier>8JMKD3MGPAW/3PFQSB8</identifier>
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		<citationkey>CosmoInabSall:2017:SiImSu</citationkey>
		<title>Single Image Super-Resolution Using Multiple Extreme Learning Machine Regressors</title>
		<format>On-line</format>
		<year>2017</year>
		<date>Oct. 17-20, 2017</date>
		<numberoffiles>1</numberoffiles>
		<size>1609 KiB</size>
		<author>Cosmo, Daniel Luis,</author>
		<author>Inaba, Fernando Kentaro,</author>
		<author>Salles, Evandro Ottoni Teatini,</author>
		<affiliation>UFES</affiliation>
		<affiliation>UFES</affiliation>
		<affiliation>UFES</affiliation>
		<editor>Torchelsen, Rafael Piccin,</editor>
		<editor>Nascimento, Erickson Rangel do,</editor>
		<editor>Panozzo, Daniele,</editor>
		<editor>Liu, Zicheng,</editor>
		<editor>Farias, Mylène,</editor>
		<editor>Viera, Thales,</editor>
		<editor>Sacht, Leonardo,</editor>
		<editor>Ferreira, Nivan,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Schiavon Porto, Marcelo,</editor>
		<editor>Vital, Creto,</editor>
		<editor>Pagot, Christian Azambuja,</editor>
		<editor>Petronetto, Fabiano,</editor>
		<editor>Clua, Esteban,</editor>
		<editor>Cardeal, Flávio,</editor>
		<e-mailaddress>danielcosmo@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<documentstage>not transferred</documentstage>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<tertiarytype>Full Paper</tertiarytype>
		<keywords>Super-Resolution, Extreme Learning Machine.</keywords>
		<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.</abstract>
		<language>en</language>
		<targetfile>PID4960161.pdf</targetfile>
		<usergroup>danielcosmo@gmail.com</usergroup>
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