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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/08.17.12.24
%2 sid.inpe.br/sibgrapi/2017/08.17.12.24.04
%T A Robust Restricted Boltzmann Machine for Binary Image Denoising
%D 2017
%A Pires, Rafael Gonçalves,
%A Santos, Daniel Felipe Silva,
%A Pereira, Luís Augusto Martins,
%A Souza, Gustavo Botelho de,
%A Levada, Alexandre Luis Magalhães,
%A Papa, João Paulo,
%@affiliation Department of Computing Federal University of São Carlos São Carlos - SP, Brazil
%@affiliation Department of Computing São Paulo State University Bauru - SP, Brazil
%@affiliation Institute of Computing University of Campinas Campinas - SP, Brazil
%@affiliation Department of Computing Federal University of São Carlos São Carlos - SP, Brazil
%@affiliation Department of Computing Federal University of São Carlos São Carlos - SP, Brazil
%@affiliation Department of Computing São Paulo State University Bauru - SP, Brazil
%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
%8 Oct. 17-20, 2017
%S Proceedings
%I IEEE Computer Society
%J Los Alamitos
%K image restoration, machine learning, restricted boltzmann machines.
%X 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.
%@language en
%3 PID4955441.pdf


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