论文信息 - Gaussian noise filter based on PCNN

Gaussian noise filter based on PCNN

The pulse coupled neural network (PCNN) has gained wide research as a new artificial neural network. It was derived directly from the study of the small mammal's visual cortex. PCNN is a model with multiple parameters, and finding the proper values of these parameters is an onerous task. So a simplified PCNN is put forward and its performance in removing Gaussian noise of image is discussed in this article. The algorithm of PCNN combined with median filter and the step-by-step modifying algorithm, which is also based on PCNN, are proposed, and the experiment results of the two algorithms are analyzed and compared with that of the median filter and the Wiener filter.

Ma Yi-de | Shih-Huang Fei | Liu Lian

[1] Reinhard Eckhorn,et al. Feature Linking via Synchronization among Distributed Assemblies: Simulations of Results from Cat Visual Cortex , 1990, Neural Computation.

[2] Clark S. Lindsey,et al. INTELLIGENT DETECTORS MODELLED FROM THE CAT'S EYE , 1997 .

[3] Jason M. Kinser,et al. Image Processing using Pulse-Coupled Neural Networks , 1998, Perspectives in Neural Computing.

[4] Jason M. Kinser,et al. Inherent features of wavelets and pulse coupled networks , 1999, IEEE Trans. Neural Networks.

[5] Heggere S. Ranganath,et al. Object detection using pulse coupled neural networks , 1999, IEEE Trans. Neural Networks.

[6] John L. Johnson,et al. PCNN models and applications , 1999, IEEE Trans. Neural Networks.