• DocumentCode
    254795
  • Title

    Improved expected patch Log likelihood scheme for image denoising

  • Author

    Shasha Zhu ; Nian Cai ; Shengru Wang ; Meilin Wang ; Shaowei Weng

  • Author_Institution
    Sch. of Inf. Eng., Guangdong Univ. of Technol., Guangzhou, China
  • fYear
    2014
  • fDate
    9-13 April 2014
  • Firstpage
    1
  • Lastpage
    2
  • Abstract
    To solve the inherent non-adaptive problem existed in the expected patch Log likelihood (EPLL), an updating process of the Gaussian mixture model introduced into the EPLL and an improved EPLL scheme via adaptive Gaussian mixture prior is proposed in this paper. Experimental results show that the proposed method outperforms the existing image denoising algorithms.
  • Keywords
    Gaussian processes; image denoising; mixture models; adaptive Gaussian mixture model; image denoising; improved EPLL scheme; improved expected patch log likelihood scheme; updating process; Educational institutions; Gaussian mixture model; Image denoising; Image restoration; Wiener filters; Gaussian mixture models; Gaussian mixture prior update; Wiener filter; image denoising;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Consumer Electronics - China, 2014 IEEE International Conference on
  • Conference_Location
    Shenzhen
  • Type

    conf

  • DOI
    10.1109/ICCE-China.2014.7029878
  • Filename
    7029878
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=254795