Pulse Coupled Neural Network Filtering Method with Parameters Determined by Particle Swarm Optimization Algorithm

This paper introduces a Pulse Coupled Neural Network (PCNN) filtering method where key parameters are determined using the Particle Swarm Optimization (PSO) algorithm. The methodology employs PSO to optimize PCNN image filtering parameters, enabling effective image denoising and enhancement. Specifically, the approach analyzes and processes pulse signals within images to extract critical features, followed by neural network-based classification and recognition for more accurate and efficient image filtering. Implementation involves initializing PSO particles with potential PCNN parameters, iteratively evaluating fitness based on denoising performance metrics, and converging toward optimal parameter sets. Key functions include adaptive threshold adjustment and synchronous pulse burst mechanisms in PCNN for preserving image details while reducing noise. The paper details implementation steps and practical applications in medical imaging, industrial inspection, and security surveillance, demonstrating significant practical value and broad potential for adoption.