Image Filtering Implementation Using Pulse Coupled Neural Network (PCNN)

The source program for implementing image filtering using Pulse Coupled Neural Network (PCNN) has been thoroughly tested and validated. This codebase implements the PCNN model architecture for image filtering applications, featuring biological neuron simulation mechanisms that process visual information through pulse propagation and coupling dynamics. The algorithm employs iterative pulse-coupled interactions between neurons to extract image features, where each pixel corresponds to a neuron with feeding inputs, linking inputs, and dynamic threshold mechanisms. Key implemented functions include neighboring neuron coupling calculations, pulse synchronization handling, and adaptive threshold adjustments that collectively enable effective noise reduction and feature preservation. The tested implementation demonstrates PCNN's effectiveness in image filtering through its ability to maintain edge details while suppressing noise, utilizing matrix operations for efficient neuron state updates and parallel processing capabilities. Should you require any additional modifications or specific functionality enhancements, please feel free to request further adjustments.