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@InProceedings{PiresSaPeSoLePa:2017:RoReBo,
               author = "Pires, Rafael Gon{\c{c}}alves and Santos, Daniel Felipe Silva and 
                         Pereira, Lu{\'{\i}}s Augusto Martins and Souza, Gustavo Botelho 
                         de and Levada, Alexandre Luis Magalh{\~a}es and Papa, Jo{\~a}o 
                         Paulo",
          affiliation = "Department of Computing Federal University of S{\~a}o Carlos 
                         S{\~a}o Carlos - SP, Brazil and Department of Computing S{\~a}o 
                         Paulo State University Bauru - SP, Brazil and Institute of 
                         Computing University of Campinas Campinas - SP, Brazil and 
                         Department of Computing Federal University of S{\~a}o Carlos 
                         S{\~a}o Carlos - SP, Brazil and Department of Computing Federal 
                         University of S{\~a}o Carlos S{\~a}o Carlos - SP, Brazil and 
                         Department of Computing S{\~a}o Paulo State University Bauru - 
                         SP, Brazil",
                title = "A Robust Restricted Boltzmann Machine for Binary Image Denoising",
            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 = "image restoration, machine learning, restricted boltzmann 
                         machines.",
             abstract = "During the image acquisition process, some level of noise is 
                         usually added to the real data mainly due to physical limitations 
                         of the acquisition sensor, and also regarding imprecisions during 
                         the data transmission and manipulation. Therefore, the resultant 
                         image needs to be processed in order to attenuate its noise 
                         without loosing details. Machine learning approaches have been 
                         successfully used for image denoising. Among such approaches, 
                         Restricted Boltzmann Machine (RBM) is one of the most used 
                         technique for this purpose. Here, we propose to enhance the RBM 
                         performance on image denoising by adding a posterior supervision 
                         before its final denoising step. To this purpose, we propose a 
                         simple but effective approach that performs a fine-tuning in the 
                         RBM model. Experiments on public datasets corrupted by different 
                         levels of Gaussian noise support the effectiveness of the proposed 
                         approach with respect to some state-of-the-art image denoising 
                         approaches.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "PID4955441.pdf",
        urlaccessdate = "2021, Jan. 21"
}


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