• DocumentCode
    2294016
  • Title

    SAR Image Despeckling Using Local Contextual Hidden Markov Model in the Contourlet Domain

  • Author

    Wang, Shuang ; Xu, Xiao ; Hou, Biao ; Jiao, Li Cheng

  • Author_Institution
    Inst. of Intelligent Inf. Process., Xidian Univ., Xi´´an
  • fYear
    2006
  • fDate
    16-19 Oct. 2006
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Synthetic aperture radar (SAR) image despeckling is an important problem in the SAR applications. A novel despeckling approach using a local contextual hidden Markov model (LCHMM) in the contourlet domain is presented in this paper. The proposed method can not only use the multiresolution and multidirection characteristics of the contourlet transform, but also exploit the local statistics and capture the intrascale dependencies of the contourlet coefficients by using LCHMM. The experiments in despeckling SAR images show that the proposed method in contrary to other methods can obtain a better trade-off between smoothing the homogeneous areas and keeping the edges and can get better visual effect
  • Keywords
    hidden Markov models; image denoising; image resolution; radar imaging; radar resolution; speckle; synthetic aperture radar; LCHMM; SAR image despeckling; contourlet transform; local contextual hidden Markov model; multiresolution characteristics; synthetic aperture radar; Context modeling; Filter bank; Hidden Markov models; Multiresolution analysis; Radar signal processing; Signal resolution; Smoothing methods; Statistics; Synthetic aperture radar; Wavelet transforms; SAR image despeckling; a local contextual hidden Markov model; contourlet transform;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Radar, 2006. CIE '06. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    0-7803-9582-4
  • Electronic_ISBN
    0-7803-9583-2
  • Type

    conf

  • DOI
    10.1109/ICR.2006.343462
  • Filename
    4148463