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Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyPiresSaPeSoLePa:2017:RoReBo
TitleA Robust Restricted Boltzmann Machine for Binary Image Denoising
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size737 KiB
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Author1 Pires, Rafael Gonçalves
2 Santos, Daniel Felipe Silva
3 Pereira, Luís Augusto Martins
4 Souza, Gustavo Botelho de
5 Levada, Alexandre Luis Magalhães
6 Papa, João Paulo
Affiliation1 Department of Computing Federal University of São Carlos São Carlos - SP, Brazil
2 Department of Computing São Paulo State University Bauru - SP, Brazil
3 Institute of Computing University of Campinas Campinas - SP, Brazil
4 Department of Computing Federal University of São Carlos São Carlos - SP, Brazil
5 Department of Computing Federal University of São Carlos São Carlos - SP, Brazil
6 Department of Computing São Paulo State University Bauru - SP, Brazil
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-17 12:24:04 :: -> administrator ::
2020-02-19 02:01:21 :: administrator -> :: 2017
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsimage restoration, machine learning, restricted boltzmann machines.
AbstractDuring 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.
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