Skip to main content
SpringerLink
Log in

SAR Image Denoising Via Fast Weighted Nuclear Norm Minimization

  • Conference paper
  • First Online:
Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. 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. 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. 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. 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. 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. 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. 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.

    Article  MathSciNet  Google Scholar 

  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. 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).

    Article  Google Scholar 

  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. Sharma LN. Information theoretic multiscale truncated SVD for multilead electrocardiogram. Comput Methods Prog Biomed. 2016;129(C):109–16.

    Article  Google Scholar 

  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. Larsen RM (2017) PROPACK-Software for large and sparse SVD calculations. http://sun.stanford.edu/~rmunk/, PROPACK.

Download references

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).

Author information

Authors and Affiliations

  1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, Jiangsu, People’s Republic of China

    Huanyue Zhao, Caiyun Wang, Jianing Wang & Panpan Huang

  2. Beijing Institute of Electronic System Engineering, Beijing, 100854, People’s Republic of China

    Xiaofei Li, Chunsheng Liu & Yuebin Sheng

Authors
  1. Huanyue Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Caiyun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaofei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chunsheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuebin Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Panpan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Caiyun Wang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, H. et al. (2020). SAR Image Denoising Via Fast Weighted Nuclear Norm Minimization. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_80

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6504-1_80

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation