Abstract
This paper develops a novel method for designing templates for discrete-time cellular neural networks (DTCNN) via an adaptive particle-swarm optimization (APSO) for gray image noise cancelation. Proper selection of the inertia weight for the APSO gives a balance between global and local searching. The research results show that a larger weight helps to increase the convergence speed while a smaller one benefits the convergence accuracy. This APSO-based method can automatically update template parameters of a discrete-time cellular neural network and optimize them to remove noise interference in polluted images. Finally, examples are given to illustrate the effectiveness of the proposed APSO-CNN methodology.
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Su, TJ., Cheng, JC., Huang, MY. et al. Applications of Cellular Neural Networks to Noise Cancelation in Gray Images Based on Adaptive Particle-swarm Optimization. Circuits Syst Signal Process 30, 1131–1148 (2011). https://doi.org/10.1007/s00034-011-9269-x
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DOI: https://doi.org/10.1007/s00034-011-9269-x