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Soft Computing

, Volume 23, Issue 22, pp 11967–11978 | Cite as

A study of sine–cosine oscillation heterogeneous PCNN for image quantization

  • Zhen YangEmail author
  • Jing Lian
  • Shouliang Li
  • Yanan Guo
  • Yide Ma
Methodologies and Application
  • 171 Downloads

Abstract

A new heterogeneous pulse-coupled neural network (HPCNN) is proposed to prune the boundary effects in image quantization. An oscillating sine–cosine pulse-coupled neural network (SC-PCNN) is combined with the morphological algorithm and two classical PCNNs which have different parameters corresponding to different image regions to form the proposed new HPCNN model (SC-HPCNN). This model retains the natural characteristics of classical PCNN while revealing its own merits; when it is used to accomplish image quantization, the quantization noise and boundary effects are removed dramatically, without significantly degrading image quality. Furthermore, experimental results also show that the proposed model outperforms previous approaches, and it operates in accordance with the characteristics of the human visual system.

Keywords

PCNN SC-HPCNN Image quantization Boundary effect 

Notes

Acknowledgements

This work is jointly supported by the Natural Science Foundation of Gansu Province (No. 18JR3RA288) and the Fundamental Research Funds for the Central Universities of China (No. lzujbky-2018-it61).

Compliance with ethical standards

Conflict of interest

The authors declare that there are no conflict of interest

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zhen Yang
    • 1
    Email author
  • Jing Lian
    • 2
  • Shouliang Li
    • 1
  • Yanan Guo
    • 1
  • Yide Ma
    • 1
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.School of Electronic and Information EngineeringLanzhou Jiaotong UniversityLanzhouChina

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