author = "Cosmo, Daniel Luis and Inaba, Fernando Kentaro and Salles, Evandro 
                         Ottoni Teatini",
          affiliation = "UFES and UFES and UFES",
                title = "Single Image Super-Resolution Using Multiple Extreme Learning 
                         Machine Regressors",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             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.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "PID4960161.pdf",
        urlaccessdate = "2021, July 26"