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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/438DG7H
Repositorysid.inpe.br/sibgrapi/2020/09.11.16.10
Last Update2020:10.01.19.25.59 rafapires@gmail.com
Metadatasid.inpe.br/sibgrapi/2020/09.11.16.10.02
Metadata Last Update2020:10.28.20.46.47 administrator
Citation KeyPiresSanSanSanPap:2020:ImDeUs
TitleImage Denoising using Attention-Residual Convolutional Neural Networks
FormatOn-line
Year2020
DateNov. 7-10, 2020
Access Date2020, Dec. 04
Number of Files1
Size1980 KiB
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Author1 Pires, Rafael Gonçalves
2 Santos, Daniel Felipe Silva
3 Santana, Marcos Cleison Silva
4 Santos, Claudio Filipe Gonçalves dos
5 Papa, João Paulo
Affiliation1 São Paulo State University (UNESP)
2 São Paulo State University (UNESP)
3 São Paulo State University (UNESP)
4 Federal University of São Carlos (UFSCAR)
5 São Paulo State University (UNESP)
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressrafapires@gmail.com
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationVirtual
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2020-10-01 19:25:59 :: rafapires@gmail.com -> administrator :: 2020
2020-10-28 20:46:47 :: administrator -> rafapires@gmail.com :: 2020
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Is the master or a copy?is the master
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Transferable1
Content TypeExternal Contribution
Tertiary TypeFull Paper
Keywordsimage restoration, deep learning.
AbstractDuring the image acquisition process, noise is usually added to the data mainly due to physical limitations of the acquisition sensor, and also regarding imprecisions during the data transmission and manipulation. In that sense, the resultant image needs to be processed to attenuate its noise without losing details. Non-learning-based strategies such as filter-based and noise prior modeling have been adopted to solve the image denoising problem. Nowadays, learning-based denoising techniques showed to be much more effective and flexible approaches, suchas Residual Convolutional Neural Networks. Here, we propose a new learning-based non-blind denoising technique named Attention Residual Convolutional Neural Network (ARCNN), and its extension to blind denoising named Flexible Attention Residual Convolutional Neural Network (FARCNN). The proposed methods try to learn the underlying noise expectation using an Attention-Residual mechanism. Experiments on public datasets corrupted by different levels of Gaussian and Poisson noise support the effectiveness of the proposed approaches against some state-of-the-art image denoising methods. ARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for Gaussian and Poisson denoising, respectively FARCNN presented very consistent results, even with slightly worsen performance compared to ARCNN.
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e-Mail (login)rafapires@gmail.com
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Next Higher Units8JMKD3MGPEW34M/43G4L9S
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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