• DocumentCode
    1932065
  • Title

    A New Algorithm for Speckle Suppression using Mathematical Morphology and Adaptive Weighted Technique

  • Author

    Jiang, Li-Hui ; Jin, Zhen-Ni ; Zhang, Fan ; Liu, Rui-Hua

  • Author_Institution
    Civil Aviation Univ. of China, Tianjin
  • Volume
    5
  • fYear
    2007
  • fDate
    19-22 Aug. 2007
  • Firstpage
    2427
  • Lastpage
    2430
  • Abstract
    In this paper, a new morphological image-cleaning algorithm that preserves thin features while removing speckle noise is presented and analyzed. In this new algorithm, the multi-scale top-hat transformation and bottom-hat transformation are added into the conventional multi-scale morphological opening and closing filtering. It differs from previous morphological filters in which it uses residual images to extract and smooth the features. The coefficients of the multi-scale top-hat transformation and bottom-hat transformation are optimized by adaptive weighted technique. This paper also shows that the new filtering algorithm preserves thin features significantly and reduces the speckle index greatly. The performance of the new filtering algorithm is superior to that of the conventional filtering methods.
  • Keywords
    feature extraction; image enhancement; mathematical morphology; speckle; adaptive weighted technique; feature extraction; image enhancement; image filtering algorithm; mathematical morphology; morphological image-cleaning algorithm; multiscale top-hat/bottom-hat transformation; speckle suppression; Algorithm design and analysis; Cybernetics; Filtering algorithms; Filters; Machine learning; Machine learning algorithms; Morphology; Noise reduction; Signal processing algorithms; Speckle; Adaptive weighted technique; Filtering; Mathematical morphology; Multi-scale; Residual images;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370553
  • Filename
    4370553