• DocumentCode
    2427646
  • Title

    Super resolution based on scale invariant feature transform

  • Author

    Yuan, Zhi ; Yan, Peimin ; Li, Sheng

  • Author_Institution
    Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai
  • fYear
    2008
  • fDate
    7-9 July 2008
  • Firstpage
    1550
  • Lastpage
    1554
  • Abstract
    In this paper, SIFT (scale invariant feature transform) algorithm is used for the image registration of super resolution to ensure a more stable and accurate registration result, and thus improve the result of super-resolution which will be realized by least squares minimization. The advantage of this approach is that the super-resolution process will have a stable result even under severe transformation conditions. SIFT method is compared with Kerenpsilas method to prove its accuracy and robustness. Fine reconstruction results are also given to show the effectiveness of this approach. Simultaneous registration method can be introduced in future work to further improve the registration accuracy.
  • Keywords
    feature extraction; image registration; image resolution; least mean squares methods; minimisation; SIFT method; image registration; least squares minimization; scale invariant feature transform; super-resolution process; Convergence; Equations; Feature extraction; Image reconstruction; Image registration; Image resolution; Least squares methods; Minimization methods; Noise robustness; Strontium;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-1723-0
  • Electronic_ISBN
    978-1-4244-1724-7
  • Type

    conf

  • DOI
    10.1109/ICALIP.2008.4590265
  • Filename
    4590265