• DocumentCode
    2826782
  • Title

    The denoising method of SAR image based on Retinex

  • Author

    Liu Dan Dan ; Tang Chun Rui

  • Author_Institution
    Coll. of Autom., Harbin Eng. Univ., Harbin, China
  • Volume
    3
  • fYear
    2010
  • fDate
    21-24 May 2010
  • Abstract
    The traditional SAR image denoising methods are not considered a shadow of image, but the shape of shadow is used for recognition. In order to take full use of SAR image dark areas information, the method of SAR image noising based on Retinex is proposed. Firstly, SAR image is decomposed into irradiated SAR image and reflected SAR image based on Retinex model; secondly, the decomposed irradiated SAR image carries through NSCT and threshold select according to context, then NSCT inverse transform; thirdly, reflected SAR image carries through NSCT, and the dark areas information and non-dark areas information are obtained by image binarization processing to irradiated SAR image according to certain threshold value. Bilateral filtering to dark areas information and context threshold select to non-dark areas information, then NSCT inverse transform; at last, image synthesizes according to Retinex model. The method takes full use of SAR image dark areas information, and the simulation results show that the denoising effects of the method is better than other methods.
  • Keywords
    image denoising; image recognition; radar imaging; synthetic aperture radar; NSCT; Retinex model; SAR image denoising method; image binarization processing; synthetic aperture radar; Context modeling; Filtering; Filters; Image denoising; Image recognition; Noise reduction; Noise shaping; Optical imaging; Optical noise; Shape; NSCT; Retinex; SAR image; denosing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Future Computer and Communication (ICFCC), 2010 2nd International Conference on
  • Conference_Location
    Wuha
  • Print_ISBN
    978-1-4244-5821-9
  • Type

    conf

  • DOI
    10.1109/ICFCC.2010.5497474
  • Filename
    5497474