• DocumentCode
    3437232
  • Title

    Robust facet model for application to speckle noise removal

  • Author

    Eom, Kie B.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., The George Washington Univ., DC, USA
  • Volume
    2
  • fYear
    2004
  • fDate
    23-26 Aug. 2004
  • Firstpage
    695
  • Abstract
    A robust facet model is developed, and applied to speckle noise removal in synthetic aperture radar (SAR) images. The parameters of a facet model are usually estimated by a least-squares (LS) method under the Gaussian assumption. In many applications, such as speckle removal in SAR images, the noise process is not Gaussian, and conventional estimators do not work. A robust estimation algorithm is developed, and applied to remove speckle noise in synthetic aperture images. Conventional adaptive filtering approaches in speckle filtering smoothes the image selectively depending on the details of underlying textures, and tend to blur details after speckle removal. In the proposed approach, the image is assumed to be composed of structural and stochastic components, and the stochastic component is modeled by a robust facet model. The proposed method is applied to real synthetic aperture images to demonstrate the validity and effectiveness of the algorithm.
  • Keywords
    adaptive filters; image denoising; least squares approximations; parameter estimation; radar imaging; stochastic processes; synthetic aperture radar; Gaussian assumption; SAR image; adaptive filtering; least-squares method; robust estimation algorithm; robust facet model; speckle filtering; speckle noise removal; synthetic aperture radar image; Adaptive filters; Additive noise; Filtering; Noise robustness; Parameter estimation; Smoothing methods; Speckle; Stochastic resonance; Synthetic aperture radar; White noise;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
  • ISSN
    1051-4651
  • Print_ISBN
    0-7695-2128-2
  • Type

    conf

  • DOI
    10.1109/ICPR.2004.1334354
  • Filename
    1334354