Title :
Particle Swarm Optimization for Image Noise Cancellation
Author :
Chen, Yue-Cheng ; Wang, Hsin-Chih ; Su, Te-Jen
Author_Institution :
Dept. of Electron. Eng., Nat. Kaohsiung Univ. of Appl. Sci.
fDate :
Aug. 30 2006-Sept. 1 2006
Abstract :
In this paper, a novel method for designing templates of cellular neural network to cancel the image noise is discussed. The discrete-time cellular neural network (DTCNN) combining with particle swarm optimization (PSO) is applied to image noise cancellation. Based on PSO method, the templates of cellular neural network is optimized to diminish noise interference in polluted image. The demonstrated examples are presented to show the better performance of the proposed methodology (PSO-CNN)
Keywords :
cellular neural nets; image denoising; interference; particle swarm optimisation; DTCNN; discrete-time cellular neural network; image noise cancellation; noise interference; particle swarm optimization; Birds; Cellular neural networks; Design methodology; Genetic algorithms; Interference; Noise cancellation; Optimization methods; Particle swarm optimization; Pollution; Space exploration;
Conference_Titel :
Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7695-2616-0
DOI :
10.1109/ICICIC.2006.136
