@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, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.58",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.58",
language = "en",
ibi = "8JMKD3MGPAW/3PF65BB",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PF65BB",
targetfile = "PID4955441.pdf",
urlaccessdate = "2024, Apr. 28"
}