Advertisement

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)

Abstract

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.

Keywords

Image denoising Synthetic aperture radar Nuclear norm Singular value decomposition 

Notes

Acknowledgments

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).

References

  1. 1.
    Yi Z, Yin D, Hu An Z, et al. SAR image despeckling based on non-local means filter. J Electron Inf Technol. 2012;34(4):950–5.Google Scholar
  2. 2.
    Ray A, Kartikeyan B, Garg S. Towards deriving an optimal approach for denoising of RISAT-1 SAR data using wavelet transform. Int J Comput Sci Eng. 2016;4(10):33–46.Google Scholar
  3. 3.
    Buades A, Coll B, Morel JM. A non-local algorithm for image denoising. In: IEEE computer society conference on, computer vision and pattern recognition. IEEE, vol. 2; 2005. p. 60–5.Google Scholar
  4. 4.
    Chen G, Xie W, Dai S. Image denoising with signal dependent noise using block matching and 3D filtering. In: International symposium on neural networks. Springer International Publishing; 2014. p. 423–30.Google Scholar
  5. 5.
    Zhou M, Song ZJ. Video background modeling based on sparse and low-rank matrix decomposition. Appl Res Comput. 2015;32(10):3175–8.Google Scholar
  6. 6.
    Zhang WQ, Zhang HZ, Zuo WM, et al. Weighted nuclear norm minimization model for matrix completion. Compu Sci. 2015;42(7):254–7.Google Scholar
  7. 7.
    Zhao J, Wang PP, Men GZ. SAR image denoising based on nonlocal similarity and low rank matrix approximation. Comput Sci. 2017;44(s1):183–7.Google Scholar
  8. 8.
    Dong W, Shi G, Li X. Nonlocal image restoration with bilateral variance estimation: a low-rank approach. IEEE Trans Image Process a Public IEEE Sig Process Soc. 2013;22(2):700–11.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gu S, Zhang L, Zuo W, et al. Weighted nuclear norm minimization with application to image denoising, In: IEEE conference on computer vision and pattern recognition. IEEE computer society; 2014. p. 2862–9.Google Scholar
  10. 10.
    Feng X, Li KX, Yu WJ, et al. Fast matrix completion algorithm based on randomized singular value decomposition and its applications. J Comput-Aided Des and Comput Graphics. 2017;29(12).CrossRefGoogle Scholar
  11. 11.
    Zuo W, Zhang L, Song C, et al. Texture enhanced image denoising via gradient histogram preservation. In: Computer vision and pattern recognition. IEEE; 2013:1203–10.Google Scholar
  12. 12.
    Sharma LN. Information theoretic multiscale truncated SVD for multilead electrocardiogram. Comput Methods Prog Biomed. 2016;129(C):109–16.CrossRefGoogle Scholar
  13. 13.
    Wang P, Cai SJ, Liu Y. Improvement of matrix completion algorithm based on random projection. J Comput Appl. 2014;34(6):1587–90.Google Scholar
  14. 14.
    Larsen RM (2017) PROPACK-Software for large and sparse SVD calculations. http://sun.stanford.edu/~rmunk/, PROPACK.

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

Personalised recommendations