@InProceedings{GonçalvesGayaDrewBote:2018:SiImDe,
               author = "Gon{\c{c}}alves, Lucas Teixeira and Gaya, Joel Felipe de Oliveira 
                         and Drews-Jr, Paulo Jorge Lilles and Botelho, Silvia Silva da 
                         Costa",
          affiliation = "{Universidade Federal do Rio Grande} and {Universidade Federal do 
                         Rio Grande} and {Universidade Federal do Rio Grande} and 
                         {Universidade Federal do Rio Grande}",
                title = "GuidedNet: Single Image Dehazing Using an End-to-end Convolutional 
                         Neural Network",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "deep learning, single image dehazing, convolutional neural 
                         networks, guided filter.",
             abstract = "Poor visibility is a common problem when capturing images in 
                         participating mediums such as mist or water. The problem of 
                         generating a haze-free image based on a hazy one can be described 
                         as image dehazing. Previous approaches dealt with this problem 
                         using physical models based on priors and simplifications. In this 
                         paper, we demonstrate that an end-to-end convolutional neural 
                         network is able to learn the dehazing process with no parameters 
                         or priors required, resulting in a more generic method. Even 
                         though our model is trained entirely with hazy indoor images, we 
                         are able to fully restore outdoor images with real haze. Also, we 
                         propose an architecture containing the novel Guided Layers, 
                         introduced in order to reduce the loss of spatial information 
                         while restoring the images. Our method outperforms other machine 
                         learning based models, yielding superior results both 
                         qualitatively and quantitatively.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
                  doi = "10.1109/SIBGRAPI.2018.00017",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3RNPSS5",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3RNPSS5",
           targetfile = "FINAL_FINAL_SIBIGRAPI.pdf",
        urlaccessdate = "2024, May 03"
}