• DocumentCode
    264925
  • Title

    Iterative Total Variation Image Deblurring with Varying Regularized Parameter

  • Author

    Binbin Hao ; Jianguang Zhu ; Yan Hao

  • Author_Institution
    Coll. of Sci., China Univ. of Pet., Qingdao, China
  • Volume
    1
  • fYear
    2014
  • fDate
    26-27 Aug. 2014
  • Firstpage
    249
  • Lastpage
    252
  • Abstract
    Total variation based model is one of the most effective method for image restoration. In this paper, we consider the total variation (TV) based regularization method and evaluate the regularization parameter for the TV based iterative forward-backward splitting (IFBS) approach. Different parameters with different iterations are obtained. The proposed adaptive iterative forward-backward splitting method does not need to know the initial value of the regularization parameter and does not require any information about the perturbation process. Experimental results demonstrate that the adaptive parameter method is efficient and provide competitive performance.
  • Keywords
    image restoration; iterative methods; IFBS approach; TV based iterative forward-backward splitting; TV based regularization method; adaptive iterative forward-backward splitting method; adaptive parameter method; image restoration; iterations; iterative total variation image deblurring; perturbation process; regularization parameter; total variation based model; Image Deblurring; Regularization Parameter; Splitting methods; Total Variation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4799-4956-4
  • Type

    conf

  • DOI
    10.1109/IHMSC.2014.68
  • Filename
    6917351