Noise Filtering in Cellular Neural Networks

  • Mikhail S. TarkovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)


A cellular neural network (CNN) with a bipolar stepwise activation function is considered. A comparative analysis of CNN learning algorithms on a given set of binary reference images for various degrees of noise (inversion of randomly selected pixels) and various cell neighborhood sizes is performed. For CNN training a local projection method, which provides much higher noisy images quality filtering than the classical local perceptron learning algorithm, is proposed.


Cellular neural network Noise filtering Perceptron training algorithm Local projection method Noisy images Cell neighborhood 


  1. 1.
    Osowski, S.: Neironnye seti dlya obrabotki informatsii (neural networks for information processing). Finansy i statistika, Moskwa (2002). (in Russian)Google Scholar
  2. 2.
    Hopfield, J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. USA 79, 2554–2558 (1982)Google Scholar
  3. 3.
    Personnaz, L., Guyon, I., Dreyfus, G.: Collective computational properties of neural networks: new learning mechanisms. Phys. Rev. A 34(5), 4217–4228 (1986)Google Scholar
  4. 4.
    Michel, A.N., Liu, D.: Qualitative analysis and synthesis of recurrent neural networks. Marcel Dekker Inc., New York (2002)Google Scholar
  5. 5.
    Tarkov, M.S.: Synapses reduction in autoassociative hopfield network. In: Proceedings of the 2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), Novosibirsk, pp. 158–160 (2017).
  6. 6.
    Chua, L.O., Yang, L.: Cellular neural networks: theory and application. IEEE Trans. Circ. Syst. 35(10), 1257–1290 (1988). CASGoogle Scholar
  7. 7.
    Pudov, S.G.: Learning of cellular neural networks. Future Gener. Comput. Syst. 17, 689–697 (2001)Google Scholar
  8. 8.
    Park, J., Kim, H.-Y., Park, Y., Lee, S.-W.: A synthesis procedure for associative memories based on space-varying cellular neural networks. Neural Netw. 14(1), 107–113 (2001)Google Scholar
  9. 9.
    Bise, R., Takahashi, N., Nishi, T.: An improvement of the design method of cellular neural networks based on generalized eigenvalue minimization. IEEE Trans. Circuits Syst.-I 50(12), 1569–1574 (2003)Google Scholar
  10. 10.
    Li, H., Liao, X., Li, C., Huang, H., Li, C.: Edge detection of noisy images based on cellular neural networks. Commun. Nonlinear Sci. Numer. Simul. 16, 3746–3759 (2011)Google Scholar
  11. 11.
    Duan, S., Hu, X., Dong, Z., Wang, L., Mazumder, P.: Memristor-based cellular nonlinear/neural network: design, analysis, and applications. IEEE Trans. Neural Netw. Learn. Syst. 26(6), 1202–1213 (2015)Google Scholar
  12. 12.
    Rosenblatt, F.: Principles of Neurodynamics. Spartan, Washington (1959)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Rzhanov Institute of Semiconductor Physics SB RASNovosibirskRussia

Personalised recommendations