SAR Image Denoising Via Fast Weighted Nuclear Norm Minimization

  • Huanyue Zhao
  • Caiyun WangEmail author
  • Xiaofei Li
  • Jianing Wang
  • Chunsheng Liu
  • Yuebin Sheng
  • Panpan Huang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


A new synthetic aperture radar (SAR) image denoising method based on fast weighted nuclear norm minimization (FWNNM) is proposed. SAR image is firstly modelled by a logarithmic additive model for modelling of the speckle. Then, the non-local similarity is used for image block matching. Next, according to the framework of the low-rank model, randomized singular value decomposition (RSVD) is introduced to replace the singular value decomposition (SVD) in weighted nuclear norm minimization (WNNM) for approximating the low-rank matrix. Finally, the gradient histogram preservation (GHP) method is employed to enhance the texture of the image. Experiments on MSTAR database show that the proposed approach is effective in SAR image denoising and the edge preserving in comparison with some traditional algorithms. Moreover, it is three times faster than WNNM method.


Image denoising Synthetic aperture radar Nuclear norm Singular value decomposition 



This work was supported in parts by the National Natural Science Foundation of China (no. 61301211), the Postgraduate Education Reform Project of Jiangsu Province (no. JGZZ17_008) and the Postgraduate Research and Practice Innovation Programme of Jiangsu Province (no. KYCX18_0295).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Huanyue Zhao
    • 1
  • Caiyun Wang
    • 1
    Email author
  • Xiaofei Li
    • 2
  • Jianing Wang
    • 1
  • Chunsheng Liu
    • 2
  • Yuebin Sheng
    • 2
  • Panpan Huang
    • 1
  1. 1.College of AstronauticsNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  2. 2.Beijing Institute of Electronic System EngineeringBeijingPeople’s Republic of China

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