Image Denoising using Attention-Residual Convolutional Neural Networks

Página do item simplificado
dc.contributor.authorPires, Rafael G. [UNESP]
dc.contributor.authorSantos, Daniel F. S. [UNESP]
dc.contributor.authorSantos, Claudio F. G.
dc.contributor.authorSantana, Marcos C. S. [UNESP]
dc.contributor.authorPapa, Joao P. [UNESP]
dc.contributor.authorIEEE
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.date.accessioned2021-06-25T15:05:13Z
dc.date.available2021-06-25T15:05:13Z
dc.date.issued2020-01-01
dc.description.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, such as 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.en
dc.description.affiliationSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil
dc.description.affiliationUniv Fed Sao Carlos, Dept Comp, Sao Carlos, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Comp, Bauru, SP, Brazil
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipPetrobras
dc.description.sponsorshipNVIDIA
dc.description.sponsorshipIdCNPq: 307066/20177
dc.description.sponsorshipIdCNPq: 427968/2018-6
dc.description.sponsorshipIdFAPESP: 2013/07375-0
dc.description.sponsorshipIdFAPESP: 2014/12236-1
dc.description.sponsorshipIdPetrobras: 2017/00285-6
dc.format.extent101-107
dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI51738.2020.00022
dc.identifier.citation2020 33rd Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi 2020). New York: Ieee, p. 101-107, 2020.
dc.identifier.doi10.1109/SIBGRAPI51738.2020.00022
dc.identifier.issn1530-1834
dc.identifier.urihttp://hdl.handle.net/11449/210333
dc.identifier.wosWOS:000651203300014
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartof2020 33rd Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi 2020)
dc.sourceWeb of Science
dc.titleImage Denoising using Attention-Residual Convolutional Neural Networksen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

Arquivos

Coleções

Bauru - FC - Faculdade de Ciências