Automatic construction of single frame super-resolution using Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Natsui:2013:IWCIA,
  author =       "Yusuke Natsui and Tomoharu Nagao",
  booktitle =    "Sixth IEEE International Workshop on Computational
                 Intelligence Applications (IWCIA 2013)",
  title =        "Automatic construction of single frame
                 super-resolution using Cartesian Genetic Programming",
  year =         "2013",
  month =        "13 " # jul,
  pages =        "149--154",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming, graphics processing units, image
                 resolution, parallel processing, CGP, GPU, HR image, LR
                 image, computational cost reduction, graphics
                 processing unit, high-resolution image, image
                 generation, image quality, low-resolution image,
                 parallel processing, pixel values, single frame
                 super-resolution method, Buildings, Computational
                 efficiency, Face, Graphics processing units, Image
                 resolution, PSNR, Training, Single Frame
                 Super-Resolution",
  DOI =          "doi:10.1109/IWCIA.2013.6624803",
  ISSN =         "1883-3977",
  abstract =     "In this paper, we propose a single-frame
                 Super-Resolution (SR) method using Cartesian Genetic
                 Programming (CGP). Our method is to learn relationship
                 of pixel values between high-resolution (HR) image and
                 low-resolution (LR) image using CGP, and we construct a
                 SR rule of generating SR image from a LR input image. A
                 single pixel and its neighbour pixels of the LR input
                 image are set to the inputs of CGP. And then, pixel
                 values of the SR image are obtained from the calculated
                 outputs of CGP. Therefore, the SR image is generated
                 from the LR input image. In addition, multiple CGP can
                 improve the quality of SR image. Because our method is
                 to perform for each pixel independently, our method is
                 suitable to parallel processing. Therefore, in order to
                 reduce computational cost, we use parallel processing
                 with graphics processing unit (GPU). Experimental
                 results show efficient processing is constructed. Our
                 method is little less quality than one conventional
                 work which is the state of the art method on image
                 quality, however to perform overwhelmingly faster than
                 the conventional work. We can construct fast and
                 accurate single-frame super-resolution.",
  notes =        "Also known as \cite{6624803}",
}

Genetic Programming entries for Yusuke Natsui Tomoharu Nagao

Citations