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This paper proposes a novel method for designing templates of Cellular Neural Network (CNN) for color image noise removal. The control of CNN systems is achieved via Particle Swarm Optimization (PSO) with Time-Varying Acceleration Coefficients (PSO-TVAC). Based on PSO-TVAC method, the proposed approach can automatically update the parameters of the templates of CNN to optimize them for diminishing noise interference in polluted image. The demonstrated examples are compared favorably with other available methods, which illustrate the better performance of the proposed PSO-TVAC-CNN methodology.
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