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An Overview of PCNN Model’s Development and Its Application in Image Processing

  • Zhen Yang
  • Jing Lian
  • Yanan Guo
  • Shouliang Li
  • Deyuan Wang
  • Wenhao Sun
  • Yide MaEmail author
Original Paper

Abstract

In this paper, recent pulse coupled neural networks (PCNN) model’s development, especially in the fields related to the image processing, were surveyed. Our review aims to provide a comprehensive and systematic analysis of selected researches from past few decades, having powerful methods to infer the area of study. In this paper, all selected references are categorized in three groups, on the basis of neurons structure, parameters setting, and the inherent characteristics of PCNN. Various applications of these models were mentioned and underlying difficulties, limitations, merits and disadvantages were discussed in applying these models. The researchers will find it helpful to choose and use the appropriate model for a better application.

Notes

Acknowledgements

This work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 61175012 and 61201421), Natural Science Foundation of Gansu Province (Grant Nos. 145RJZA181 and 1208RJZA265), Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20110211110026), and the Fundamental Research Funds for the Central Universities of China (Grant Nos. lzujbky-2013-k06 and lzujbky-2015-196).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflicts of interest.

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Copyright information

© CIMNE, Barcelona, Spain 2018

Authors and Affiliations

  • Zhen Yang
    • 1
  • Jing Lian
    • 1
  • Yanan Guo
    • 1
  • Shouliang Li
    • 1
  • Deyuan Wang
    • 1
  • Wenhao Sun
    • 1
  • Yide Ma
    • 1
    Email author
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina

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