| Abstract: | Recently, pulse coupled neural network (PCNN) attracts much attention in image denoising as a nonlinear filtering technique. The PCNN‐based anisotropic diffusion (PCNN‐AD) method has been proposed previously for flicker noise reduction and its effectiveness has been demonstrated. Using the visual characteristics of PCNN, PCNN‐AD has also solved the problem of AD that AD is not able to suppress the isolate noise. However, there are still two drawbacks in PCNN‐AD, that is, time consuming and PCNN parameters' estimation. In order to improve the efficiency and the denoising performance of PCNN‐AD, a PCNN‐based method with an adaptive Pareto genetic algorithm (GA‐PCNN) has been proposed to restrain from additive white Gaussian noise (AWGN) in this paper. GA‐PCNN firstly integrates the PCNN and AD as a parallel system, then, optimizes the parameters of a simplified PCNN by the adaptive Pareto GA. Experimental results indicate that GA‐PCNN has better performances than the previous denoising techniques, i.e. median filter, Wiener filter, AD filter, and PCNN‐AD. The effectiveness of GA‐PCNN on AWGN reduction and edge preservation are shown finally. The results will also contribute to denoising in CMOS image sensors in the future. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. |
