Skip to main content

Advertisement

SpringerLink
Log in

A novel energy functional minimization model for speckle noise removal

  • Published:
Optoelectronics Letters Aims and scope Submit manuscript

Abstract

In this paper, a novel energy functional minimization model is proposed for ultrasound images denoising. A controllable regularized term and the variational method are employed in the process of speckle noise. This model not only improves the plasticity of the model, but also improves the effect and efficiency of noise removal. The new model has different diffusion performance in different regions. At the same time, the diffusion performance is related to the parameters introduced by the proposed model. Numerical simulation results show that different parameters have different denoising effects, and the proposed model for speckle noise removal is superior to traditional models.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Loupas T, Mcdicken W and Allan P.L, IEEE Trans. Circ. Syst. 36, 129 (1989).

    Article  Google Scholar 

  2. Jin J, Liu Y, Wang Q and Yi S, IEEE Instrumentation and Measurement Technology Conference, 255 (2012).

  3. Chan T and Shen J, SIAM Journal on Applied Mathematics 62, 1019 (2001).

    MathSciNet  Google Scholar 

  4. Chan T, Kang S and Shen J, SIAM Journal on Applied Mathematics 63, 564 (2002).

    MathSciNet  Google Scholar 

  5. Yu Y and Acton S, IEEE Transactions on Image Processing 11, 1260 (2002).

    Article  ADS  MathSciNet  Google Scholar 

  6. Krissian K, Westin C, Kikinis R and Vosburgh K, IEEE Transactions on Image Processing 16, 1412 (2007).

    Article  ADS  MathSciNet  Google Scholar 

  7. Shi M, Han T and Liu S, Signal Processing 126, 65 (2016).

    Article  Google Scholar 

  8. Jin Z and Yang X, Journal of Mathematical Imaging and Vision 39, 62 (2011).

    Article  MathSciNet  Google Scholar 

  9. Rudin L, Osher S and Fatemi E, Physica D Nonlinear Phenomena 60, 259 (1992).

    Article  ADS  MathSciNet  Google Scholar 

  10. Kang M, Kang M and Jung M, Journal of Scientific Computing 72, 172 (2017).

    Article  MathSciNet  Google Scholar 

  11. Zhang H, Tang L, Fang Z, Xiang C and Li C, Signal Processing 143, 69 (2018).

    Article  Google Scholar 

  12. Mei J, Huang T, Wang S and Zhao X, Journal of the Franklin Institute 355, 574 (2018).

    Article  Google Scholar 

  13. Krissian K, Kikinis R, Westin C and Vosburgh, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 547 (2005).

  14. Costanzino N, http://www.lems.brown.edu/nc, 2002.

  15. Fehrenbach J and Mirebeau J, Journal of Mathematical Imaging and Vision 49, 123 (2014).

    Article  MathSciNet  Google Scholar 

  16. Hacini M, Hachouf F and Djemal K, Signal Processing 103, 214 (2014).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Shenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and Statistics, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China

    Bo Chen  (陈波), Jin-bin Zou  (邹锦彬), Wen-sheng Chen  (陈文胜) & Meng-yun Zhang  (张梦云)

Authors
  1. Bo Chen  (陈波)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jin-bin Zou  (邹锦彬)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-sheng Chen  (陈文胜)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Meng-yun Zhang  (张梦云)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Chen  (陈波).

Additional information

This paper has been supported by the Natural Science Foundation of Guangdong Province (No.2018A030313364), the Special Innovation Projects of Universities in Guangdong Province (No.2018KTSCX197), the Science and Technology Planning Project of Shenzhen City (No.JCYJ20180305125609379), and the China Scholarship Council Project (No.201508440370). This paper was presented in part at the Chinese Conference on Pattern Recognition and Computer Vision, Guangzhou, 2018. This paper was recommended by the program committee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, B., Zou, Jb., Chen, Ws. et al. A novel energy functional minimization model for speckle noise removal. Optoelectron. Lett. 15, 386–390 (2019). https://doi.org/10.1007/s11801-019-8202-6

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11801-019-8202-6

Search

Navigation