Pulse coupled neural network (PCNN) has a specific feature that the fire of

one neuron ca pture its adjacent neurons to fire due to their spatial proximity and

intensity similarity. In this paper, it is indicated that this feature itself is a very good

mechanism for image filtering when the image is damaged with pep and salt (PAS) type

noise. An adaptive filtering method, in which the noisy pixels are first located and then

filtered based on the output of the PCNN, is presented. The threshold function of a neuron

in the PCNN is designed when it is used for filtering random PAS and extreme PAS noise

contaminated image respectively. The filtered image has no distortion for noisy pixels and

only less mistiness for non-noisy pixels, compared with the conventional window-based

filtering method. Excellent experimental results show great effectiveness and efficiency of

the proposed method, especially for heavy-noise contaminated images-Pulse coupled neural network (PCNN) has a specific feature that the fire of

one neuron can capture its adjacent neurons to fire due to their spatial proximity and

intensity similarity. In this paper, it is indicated that this feature itself is a very good

mechanism for image filtering when the image is damaged with pep and salt (PAS) type

noise. An adaptive filtering method, in which the noisy pixels are first located and then

filtered based on the output of the PCNN, is presented. The threshold function of a neuron

in the PCNN is designed when it is used for filtering random PAS and extreme PAS noise

contaminated image respectively. The filtered image has no distortion for noisy pixels and

only less mistiness for non-noisy pixels, compared with the conventional window-based

filtering method. Excellent experimental results show great effectiveness and efficiency of

the proposed method, especially for heavy-noise contaminated images