Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Cellular Neural Networks for Gray Image Noise Cancellation Based on a Hybrid Linear Matrix Inequality and Particle Swarm Optimization Approach

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

This paper describes a technique for gray image noise cancellation. This method employs linear matrix inequality (LMI) and particle swarm optimization (PSO) based on cellular neural networks (CNN).We use two images that one is desired image and the other is corrupted to find the CNN template. The Lyapunov stability theorem is employed to derive the criterion for uniqueness and global asymptotic stability of the CNN equilibrium point. The current study characterizes the template design problem as a standard LMI problem and the optimization parameters of the templates are carried out by PSO. Finally, the examples are given to illustrate the effectiveness of the proposed method.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Boyd S, Ghaoui LE, Feron E, Balakrishnan V (1994) Linear matrix inequalities in system and control theory, studies in applied mathematics. SIAM, Philadelphia

    Google Scholar 

  2. Chua LO, Yang L (1988) Cellular neural networks: theory. IEEE Trans Circuits Syst 35: 1257–1272

    Article  MATH  MathSciNet  Google Scholar 

  3. Chua LO, Yang L (1988) Cellular neural networks: applications. IEEE Trans Circuits Syst 35: 1273–1290

    Article  MathSciNet  Google Scholar 

  4. Chua LO, Roska T (2002) Cellular neural networks and visual computing. Cambridge University Press, New York

    Book  Google Scholar 

  5. Gahinet P, Nemirovskii A, Laub A, Chilali M (1995) LMI control toolbox: for use with MATLAB. The MATH Works Inc., Portola Valley

    Google Scholar 

  6. Gilli M (1993) A Lyapunov function approach to the study of the stability of cellular neural networks. IEEE Int Symp Circuits Syst 4: 2584–2587

    Google Scholar 

  7. Heppner F, Grenander U (1990) A stochastic nonlinear model for coordinated bird flocks, the ubiquity of chaos. AAAS Publications, Washington, DC

    Google Scholar 

  8. He Y, Wu M, She JH (2006) An improved global asymptotic stability criterion for delayed cellular neural networks. IEEE Trans Neural Netw 17: 250–252

    Article  Google Scholar 

  9. Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf 4: 1942–1948

    Google Scholar 

  10. Liang X, Tan W, Wan Z, Yang D (2008) A novel criterion for glob al asymptotic stability of cellular neural networks with time delays. In: 7th World congress on intelligent control and automation WCICA’08, June 2008, pp 4430–4433

  11. Lin YJ, Hou CL, Su TJ (2009) Cellular neural network for noise cancellation of gray image based on hybrid linear matrix inequality and particle swarm optimization. In: 2009 International conference on new trends in information and service science, Beijing, China, 30 June–2 July 2009, pp 613–617

  12. Lopez PD, Vilarino LD, Cabello D (2000) Design of multilayer discrete time cellular neural networks for image processing tasks based on genetic algorithms. IEEE Int Symp Circuits Syst 4: 133–136

    Google Scholar 

  13. Matei RP (2000) Image processing using hysteretic cellular neural networks. IEEE Int Symp Circuits Syst 4: 129–132

    Google Scholar 

  14. Ming L, Min L (2004) The robustness design of templates of CNN for detecting inner corners of objects in gray-scale images. IEEE Int Conf Commun Circuits Syst 2: 1090–1093

    Google Scholar 

  15. Shi Y, Eberhart R (1998) A modified particle optimizer. In: Proceedings of the 1998 IEEE world congress on computational intelligence, May 1998, pp 69–73

  16. Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. In: Proceedings of the 7th international conference on evolutionary programming VII. Lecture notes in computer science, pp 591–600

  17. Singh V (2004) Global asymptotic stability of cellular neural networks with unequal delays: LMI approach. Electron Lett 40: 548–549

    Article  Google Scholar 

  18. Zhang H, Wang Z (2007) Global asymptotic stability of delayed cellular neural networks. IEEE Trans Neural Netw 18: 947–950

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, 807, Taiwan, ROC

    Te-Jen Su, Ming-Yuan Huang, Chia-Ling Hou & Yu-Jen Lin

  2. College of Information & Technology, Kun Shan University, Yung-Kong, 71063, Taiwan, ROC

    Te-Jen Su

Authors
  1. Te-Jen Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming-Yuan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chia-Ling Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu-Jen Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Te-Jen Su.

Additional information

This study was supported financially in part by grants from the NSC-2009-2221-E-151-057, ROC.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Su, TJ., Huang, MY., Hou, CL. et al. Cellular Neural Networks for Gray Image Noise Cancellation Based on a Hybrid Linear Matrix Inequality and Particle Swarm Optimization Approach. Neural Process Lett 32, 147–165 (2010). https://doi.org/10.1007/s11063-010-9150-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-010-9150-0

Keywords

Search

Navigation