Image Processing Based On Pulse-Coupled Neural Network

Pulse-Coupled Neural Network (Simplified as PCNN) is a model built through the simulation of the outbursts of synchronous pulses in the visual layer of a cat's cerebra. It is called the third generation artificial neural network. Much attention has been paid to the mechanism of PCNN and its applications. More and more researchers have also paid attention to it at home. This thesis does some works: (1) The mechanism and behavior of PCNN are analyzed, and the PCNN is applied to image processing,radar,sonar,biomedicine,signal processing and so on. (2) Two methods for image noise (Gaussian noise and salt & pepper noise) based on PCNN are presented. (3) The mechanism of cooperation and competition is added to the PCNN, New algorithms for image segmentation and image edge detection based on the PCNN and information entropy are given. Compared to the traditional PCNN and other algorithms, the new algorithms have several advantages, such as clear profile and abundant details. Finnally, we summarize the done works, and list some disadvantages and unsolved problems.