• DocumentCode
    1950402
  • Title

    Single Image Super-Resolution Based on Support Vector Regression

  • Author

    Li, Dalong ; Simske, Steven ; Mersereau, Russell M.

  • Author_Institution
    Hewlett-Packard Lab., Fort Collins
  • fYear
    2007
  • fDate
    12-17 Aug. 2007
  • Firstpage
    2898
  • Lastpage
    2901
  • Abstract
    Motivated by the success of support vector regression (SVR) in blind image deconvolution, we apply SVR to single-frame super-resolution. Initial results show that even when trained on as little as a single image, SVR is able to learn a generally applicable model that can super-resolve dissimilar images.
  • Keywords
    image resolution; learning (artificial intelligence); regression analysis; support vector machines; machine learning; single image super-resolution; single-frame super-resolution; support vector regression; Deconvolution; Discrete cosine transforms; Discrete wavelet transforms; Filtering; High-resolution imaging; Image resolution; Interpolation; Low pass filters; PSNR; Signal resolution;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2007. IJCNN 2007. International Joint Conference on
  • Conference_Location
    Orlando, FL
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1379-9
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2007.4371420
  • Filename
    4371420
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1950402