• DocumentCode
    3021914
  • Title

    Hybrid denoising algorithm of NSCT and improved NL-means method to SAR images

  • Author

    Zhao HongYu ; Wang Qingping ; Wu WeiWei ; Yuan NaiChang

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Nat. Univ. of Defense Technol., Changsha, China
  • fYear
    2013
  • fDate
    20-22 Dec. 2013
  • Firstpage
    879
  • Lastpage
    882
  • Abstract
    To efficiently preserve tiny details and sharpness information of synthetic aperture radar (SAR) images while clearly remove the speckles, a despeckling method is proposed in this letter. Firstly, the SAR image is creatively separated to two parts: the texture region and flat region by using the decomposition with non-subsampled contourlet transform (NSCT) and mask estimation iteration algorithm. secondly, in the texture region, a new method of integrating the non-local means (NL-means) with block matching is used to preserve the sharpness and tiny details of SAR images; finally, a big searching window is utilized only in the flat region to remove the noise to a great extent. The experimental results show that both the visual quality and evaluation index of the proposed method outperform the traditional three methods: enhance Lee filtering (ELF), the bilateral Filtering (BF) and the improved NL-means.
  • Keywords
    estimation theory; image denoising; image texture; radar imaging; synthetic aperture radar; BF; ELF; NSCT; SAR images; bilateral filtering; block matching; despeckling method; enhance Lee filtering; evaluation index; flat region; hybrid denoising algorithm; improved NL-means method; mask estimation iteration algorithm; nonlocal means; nonsubsampled contourlet transform; synthetic aperture radar images; texture region; visual quality; Geophysical measurement techniques; Ground penetrating radar;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference on
  • Conference_Location
    Shengyang
  • Print_ISBN
    978-1-4799-2564-3
  • Type

    conf

  • DOI
    10.1109/MEC.2013.6885183
  • Filename
    6885183