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Dynamic properties of feed-forward neural networks and application in contrast enhancement for image

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  • Zhang, Chunrui
  • Zhang, Xianhong
  • Zhang, Yazhou

Abstract

This paper is concerned with three neurons feed-forward neural network model and more specifically with the study of dynamical behavior of the codimension one nilpotent singularity and 1:1 resonant Hopf bifurcation and outline possible image processing applications. Three neurons dynamical feed-forward neural networks use cross-coupling and feed-forward-coupling to form an nonlinear dynamic neural oscillator with the time delay. The theoretical basis of the pitchfork and 1:1 resonant Hopf bifurcation of feed-forward neural networks with delay is carried out and the analytical formulas are derived to define the various states of the system. The ultimate goal is to understand the dynamics and seek the application in image processing. It is shown that each of these states has a significant impact on the quality of the resulting image contrast enhancement. As application, aiming at the characteristics of remote sensing images with low-contrast and poor resolution textual information, an image enhancement method is presented. We show theoretically and numerically that the gray scale remote sensing image picture contrast is strongly enhanced even if this one is initially very small. The results show that the algorithm can significantly improve the visual impression of the image. Compared with the proposed algorithms in recent years, the information entropy are significantly improved.

Suggested Citation

  • Zhang, Chunrui & Zhang, Xianhong & Zhang, Yazhou, 2018. "Dynamic properties of feed-forward neural networks and application in contrast enhancement for image," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 281-290.
    • Handle: RePEc:eee:chsofr:v:114:y:2018:i:c:p:281-290
    DOI: 10.1016/j.chaos.2018.07.016
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    References listed on IDEAS

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    1. Zheng, Baodong & Zhang, Yazhuo & Zhang, Chunrui, 2008. "Global existence of periodic solutions on a simplified BAM neural network model with delays," Chaos, Solitons & Fractals, Elsevier, vol. 37(5), pages 1397-1408.
    2. Yu, Haitao & Wang, Jiang & Liu, Qiuxiang & Sun, Jianbing & Yu, Haifeng, 2013. "Delay-induced synchronization transitions in small-world neuronal networks with hybrid synapses," Chaos, Solitons & Fractals, Elsevier, vol. 48(C), pages 68-74.
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    Cited by:

    1. Golnoosh Babaei & Shahrooz Bamdad, 2021. "A New Hybrid Instance-Based Learning Model for Decision-Making in the P2P Lending Market," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 419-432, January.
    2. Wang, Wenlong & Lin, Xiao & Zhang, Chunrui, 2021. "Resonant bifurcation of feed-forward chains and application in image contrast enhancement," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 187(C), pages 294-307.
    3. Gao, Shang & Peng, Keyu & Zhang, Chunrui, 2021. "Existence and global exponential stability of periodic solutions for feedback control complex dynamical networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

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