@InProceedings{PiresSanSanSanPap:2020:ImDeUs,
               author = "Pires, Rafael Gon{\c{c}}alves and Santos, Daniel Felipe Silva and 
                         Santana, Marcos Cleison Silva and Santos, Claudio Filipe 
                         Gon{\c{c}}alves dos and Papa, Jo{\~a}o Paulo",
          affiliation = "{S{\~a}o Paulo State University (UNESP)} and {S{\~a}o Paulo 
                         State University (UNESP)} and {S{\~a}o Paulo State University 
                         (UNESP)} and {Federal University of S{\~a}o Carlos (UFSCAR)} and 
                         {S{\~a}o Paulo State University (UNESP)}",
                title = "Image Denoising using Attention-Residual Convolutional Neural 
                         Networks",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "image restoration, deep learning.",
             abstract = "During 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.",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00022",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00022",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/438DG7H",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/438DG7H",
           targetfile = "PID6634881.pdf",
        urlaccessdate = "2024, Apr. 29"
}