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@InProceedings{NepomucenoSilv:2021:EvLoFu,
               author = "Nepomuceno, Raphael and Silva, Michel M.",
          affiliation = "{Universidade Federal de Vi{\c{c}}osa} and {Universidade Federal 
                         de Vi{\c{c}}osa}",
                title = "Evaluating Loss Functions for Illustration Super-Resolution Neural 
                         Networks",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "super-resolution,convolutional neural networks,deep 
                         learning,computer vision.",
             abstract = "As display technologies evolve and high-resolution screens become 
                         more available, the desirability of images and videos with high 
                         perceptual quality grows in order to properly utilize such 
                         advances. At the same time, the market for illustrated mediums, 
                         such animations and comics, has been in steady growth over the 
                         past years. Based on these observations, we were motivated to 
                         explore the super-resolution task in the niche of drawings. In 
                         absence of original high-resolution imagery, it is necessary to 
                         use approximate methods, such as interpolation algorithms, to 
                         enhance low-resolution media. Such methods, however, can produce 
                         undesirable artifacts in the reconstruct images, such as blurring 
                         and edge distortions. Recent works have successfully applied deep 
                         learning to this task, but such efforts are often aimed at 
                         real-world images and do not take in account the specifics of 
                         illustrations, which emphasize lines and employ simplified 
                         patterns rather than complex textures, which in turn makes visual 
                         artifacts introduced by algorithms easier to spot. With these 
                         differences in mind, we evaluated the effects of the choice of 
                         loss functions in order to obtain accurate and perceptually 
                         pleasing results in the super-resolution task for comics, 
                         cartoons, and other illustrations. Experimental evaluations have 
                         shown that a loss function based on edge detection performs best 
                         in this context among the evaluated functions, though still 
                         showing room for further improvements.",
  conference-location = "Gramado (Virtual), Brazil",
      conference-year = "October 18th to October 22nd, 2021",
             language = "en",
           targetfile = "paper.pdf",
        urlaccessdate = "2022, Jan. 21"
}


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