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Cellular Neural Networks for Gray Image Noise Cancellation Based on a Hybrid Linear Matrix Inequality and Particle Swarm Optimization Approach

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Abstract

This paper describes a technique for gray image noise cancellation. This method employs linear matrix inequality (LMI) and particle swarm optimization (PSO) based on cellular neural networks (CNN).We use two images that one is desired image and the other is corrupted to find the CNN template. The Lyapunov stability theorem is employed to derive the criterion for uniqueness and global asymptotic stability of the CNN equilibrium point. The current study characterizes the template design problem as a standard LMI problem and the optimization parameters of the templates are carried out by PSO. Finally, the examples are given to illustrate the effectiveness of the proposed method.

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Authors and Affiliations

  1. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, 807, Taiwan, ROC

    Te-Jen Su, Ming-Yuan Huang, Chia-Ling Hou & Yu-Jen Lin

  2. College of Information & Technology, Kun Shan University, Yung-Kong, 71063, Taiwan, ROC

    Te-Jen Su

Authors
  1. Te-Jen Su
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  2. Ming-Yuan Huang
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  3. Chia-Ling Hou
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  4. Yu-Jen Lin
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Corresponding author

Correspondence to Te-Jen Su.

Additional information

This study was supported financially in part by grants from the NSC-2009-2221-E-151-057, ROC.

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Su, TJ., Huang, MY., Hou, CL. et al. Cellular Neural Networks for Gray Image Noise Cancellation Based on a Hybrid Linear Matrix Inequality and Particle Swarm Optimization Approach. Neural Process Lett 32, 147–165 (2010). https://doi.org/10.1007/s11063-010-9150-0

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  • DOI: https://doi.org/10.1007/s11063-010-9150-0

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