Cellular Neural Network for Noise Cancellation of Gray Image Based on Hybrid Linear Matrix Inequality and Particle Swarm Optimization

Author

Su, Te-Jen ; Lin, Yu-Jen ; Hou, Chia-Ling

Author_Institution

Dept. of Electron. Eng., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung, Taiwan

fYear

2009

fDate

June 30 2009-July 2 2009

Firstpage

613

Lastpage

617

Abstract

In this paper, the technique of noise cancellation for gray image is presented by employing linear matrix inequality (LMI) and particle swarm optimization (PSO) based on cellular neural networks (CNN). A criterion for global asymptotic stability of CNN is presented based on the Lyapunov stability theorem, and the problem of image noise cancellation can be characterized in terms of LMIs. Based on stability conditions of LMI, the parameter of templates are obtained via PSO. The examples are given to illustrate the effectiveness of the proposed method.

Keywords

Lyapunov methods; asymptotic stability; cellular neural nets; image denoising; linear matrix inequalities; particle swarm optimisation; Lyapunov stability theorem; cellular neural network; global asymptotic stability; gray image; hybrid linear matrix inequality; image noise cancellation; particle swarm optimization; Acceleration; Asymptotic stability; Cellular neural networks; Electronic mail; Image processing; Linear matrix inequalities; Lyapunov method; Noise cancellation; Particle swarm optimization; Process design; cellular neural networks; image; linear matrix inequality; noise cancellation; particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

New Trends in Information and Service Science, 2009. NISS '09. International Conference on

Conference_Location

Beijing

Print_ISBN

978-0-7695-3687-3

Type

conf

DOI

10.1109/NISS.2009.238

Filename

5260949