Morphology open opreation in unit-linking pulse coupled neural network for image processing

Abstract

This paper describes that under certain operations, the parallel pulse transmission behavior of unit-linking PCNN (pulse coupled neural network) for image processing, which we have developed from PCNN based on biological experimental results, is equal to the open operation of mathematic morphology. The open operation of unit-linking PCNN based on its parallel pulse transmission behavior can be efficiently used for image processing, such as reducing the pulse noise of binary images. A new pulse-noise-reducing approach based on the open operation of unit-linking PCNN is proposed in this paper. The results of computer simulations show that the visual effects of retrieval images of binary pulse-noise images based on the open operation of unit-linking PCNN are better than those based on the median filter, a tradition approach for reducing image pulse noise. For binary images polluted by any other kinds of noise, such as white Guassian noise, after having turned the original noisy images into binary noisy images, using the approach proposed in this paper also can reduce the noise. This paper bridges the gap between image processing based on mathematical morphology and image processing based on unit-linking PCNN.