• DocumentCode
    3448127
  • Title

    Image denoising using multiple wavelet representations and local contextual hidden Markov model

  • Author

    Zhang, Wei ; Wei, Ke-Tai ; Liu, Xi-Mei

  • Author_Institution
    Coll. of Autom. & Electron. Eng., Qingdao Univ. of Sci. & Technol., Qingdao
  • fYear
    2007
  • fDate
    15-18 Dec. 2007
  • Firstpage
    156
  • Lastpage
    161
  • Abstract
    Wavelet-domain local contextual hidden Markov model (LCHMM) can exploit both the local statistics and the intrascale dependencies of wavelet coefficients at a low computational complexity. Multiple wavelet representations have excellent performance in image denoising. In this paper, combining the multiple wavelet representations with the LCHMM and using their advantages in image denoising, we propose a new image denoising algorithm, called M-LCHMM. It is simple and effective. Simulation results show that the proposed M-LCHMM can achieve the state-of-the-art image denoising performance at the low computational complexity.
  • Keywords
    computational complexity; hidden Markov models; image denoising; wavelet transforms; M-LCHMM; computational complexity; image denoising; local contextual hidden Markov model; local statistics; multiple wavelet representations; wavelet coefficients; Computational complexity; Context modeling; Degradation; Discrete wavelet transforms; Hidden Markov models; Image denoising; Noise reduction; Robotics and automation; Statistics; Wavelet coefficients; Image denoising; Local Contextual Hidden Markov Model; multiple wavelet representations;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robotics and Biomimetics, 2007. ROBIO 2007. IEEE International Conference on
  • Conference_Location
    Sanya
  • Print_ISBN
    978-1-4244-1761-2
  • Electronic_ISBN
    978-1-4244-1758-2
  • Type

    conf

  • DOI
    10.1109/ROBIO.2007.4522152
  • Filename
    4522152