An adaptive method for image filtering with pulse-coupled neural networks
Pulse coupled neural network (PCNN) has a specific feature that the fire of one neuron can capture its adjacent neuron 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 pet and salt type noise (PASN). 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 for random PASN and extreme PASN 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 the heavily noise contaminated images.