Reference TypeConference Proceedings
Citation KeyPiresSanSanSanPap:2020:ImDeUs
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)
TitleImage Denoising using Attention-Residual Convolutional Neural Networks
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
Book TitleProceedings
DateNov. 7-10, 2020
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationVirtual
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.
Tertiary TypeFull Paper
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History2020-10-01 19:25:59 :: -> administrator :: 2020
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