PCNN Model Analysis And Its Automatic Parameters Determination In Image Processing

With features of neuron synchronous pulse burst and capture, the Pulse Coupled Neural Network (PCNN) model has been widely applied in processing areas like digital image segmentation, edge detection, noise filtering, image enhancement, retrieval etc. and it has been the hot research area in the field of biomedical image analysis, remote sensing image processing, military target detection and traffic image processing. Yet features of PCNN image processing have been improved continually, there are still some key technical difficulties that has roused attentions from researchers home and abroad. Difficulties like mathematical properties of PCNN model itself and effects of parameters adaptive setting of the model on neuron have become the difficult and hot research subject.Guided by the system equation of PCNN model and by using mathematical iteration method and the discrete system analysis method neuron firing mechanism of PCNN model was analyzed under two kinds of state, coupling and without coupling. This paper presented the mathematical expression and modifier formula of neuron firing time, revealed the mathematical coupling feature of PCNN model itself, the step firing and the coverage influence it had on the biological characteristics of PCNN. The mechanism caused by that phenomenon as well as ways to eliminate have been analyzed. And based on what have been researched, this paper analyzed the situation that the neuron firing was influenced by the status of neighborhood neuron firing, brought out the mathematical expression of the lead of neuron firing timing and further put out the parameter adaptive setting method of PCNN based on the idea of eliminating the mathematical coupling feature of PCNN model and with two restrictions:the max gray and the minimum time firing. Based on the study of the law that neuron firing time is affected by network parameters and neighborhood neuron firing status, the writer presented an improved PCNN model and applied it into areas like image segmentation and noise filtering and a new model based on image edge detection of PCNN was also presented. In terms of those three ways of image processing, parameters adaptive setting of them have been analyzed and discussed separately. The main work as follows:1. By mathematical analysis of the discrete equation of PCNN model, the mathematical expression and modifier formula of neuron firing time under two kinds of state, coupling and without coupling were given. The research revealed the phenomenon that the theoretic neuron firing time disaccord with the actual neuron firing time, put out concepts of "mathematics firing" and "diagram of step firing". The relationship between step features of neuron firing and network parameter was analyzed in detail.2. From perspectives that neuron firing time itself as well as effects of neighborhood neuron on coupling effect of neuron themselves are influenced by firing step, the writer analyzed effects of the mathematical coupling feature of the PCNN model on the feature of network biology, discussed the mechanism caused by interference and influence in detail, put out the parameter adaptive setting method based on the idea of eliminating the mathematical coupling feature of PCNN model and the algorithm performance was analyzed too.3. Parameters of PCNN model was put in order and simulation for the process of modulation of subsystem of the model was analyzed and from which the information that how did parameters determination of the subsystem influence network pulse burst feature was given. Then an improved PCNN model with a "fine" pulse burst feature was put forward. Features of the improved model were analyzed and parameter adaptive setting method was provided.4. By applying the improved PCNN model with a "fine" pulse burst feature, mentioned above, into areas of image segmentation and noise filtering, parameters adaptive setting in different applications were discussed and a new image edge detection model based on PCNN as well as parameters adaptive setting methods were put out.