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@InProceedings{SeibelJrGoldRoch:2015:FaEfGe,
               author = "Seibel Junior, Hilario and Goldenstein, Siome and Rocha, 
                         Anderson",
          affiliation = "Instituto Federal do Espirito Santo, Universidade Estadual de 
                         Campinas and {Universidade Estadual de Campinas} and {Universidade 
                         Estadual de Campinas}",
                title = "Fast and Effective Geometric K-Nearest Neighbors Multi-Frame 
                         Super-Resolution",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "super-resolution, geometric k-NN, multi-frame, burst, mobile 
                         devices.",
             abstract = "Multi-frame super-resolution is possible when there is motion and 
                         non-redundant information from a sequence of low-resolution input 
                         images. Remote sensors, surveillance videos and modern mobile 
                         phones are examples of devices able to easily gather multiple 
                         images of a same scene. However, combining a large number of 
                         frames into a higher resolution image may not be computationally 
                         feasible by complex super-resolution techniques. We discuss herein 
                         a set of simple and effective high-performance algorithms to 
                         fastly super-resolve several low-resolution images in an always-on 
                         low-power environment, with possible applications in mobile 
                         computing, forensics, and biometrics. The algorithms rely on 
                         geometric k-nearest neighbors to decide which information to 
                         consider in each high-resolution pixel, have a low memory 
                         footprint and run in linear time as we increase the number of 
                         low-resolution input images. Finally, we suggest a minimum number 
                         of input images for multi-frame super-resolution, considering that 
                         we expect a good response as fast as possible.",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
           targetfile = "PID3771795.pdf",
        urlaccessdate = "2021, Mar. 02"
}


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