%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/08.21.19.04
%2 sid.inpe.br/sibgrapi/2017/08.21.19.04.11
%@doi 10.1109/SIBGRAPI.2017.59
%T Single Image Super-Resolution Using Multiple Extreme Learning Machine Regressors
%D 2017
%A Cosmo, Daniel Luis,
%A Inaba, Fernando Kentaro,
%A Salles, Evandro Ottoni Teatini,
%@affiliation UFES
%@affiliation UFES
%@affiliation UFES
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ, Brazil
%8 17-20 Oct. 2017
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Super-Resolution, Extreme Learning Machine.
%X 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.
%@language en
%3 PID4960161.pdf