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

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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.

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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).

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China

    Zhen Yang, Shouliang Li, Yanan Guo & Yide Ma

  2. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, Gansu, China

    Jing Lian

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  1. Zhen Yang
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  2. Jing Lian
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  3. Shouliang Li
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  4. Yanan Guo
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Correspondence to Zhen Yang.

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Yang, Z., Lian, J., Li, S. et al. A study of sine–cosine oscillation heterogeneous PCNN for image quantization. Soft Comput 23, 11967–11978 (2019). https://doi.org/10.1007/s00500-018-03752-z

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