Gaussian Noise Filtering Using Pulse-Coupled Neural Networks

This is a Gaussian noise filtering program based on pulse-coupled neural networks (PCNN), including comprehensive source code and experimental results. The program's primary objective is to implement a filtering algorithm using PCNN methodology to effectively remove Gaussian noise from images or signals, thereby enhancing their quality and clarity. The source code provides detailed implementation aspects, featuring key components such as neuron modeling, synaptic connection matrices, and iterative firing mechanisms characteristic of PCNN architectures. The implementation includes noise threshold calculations, pulse synchronization algorithms, and adaptive filtering parameters that enable dynamic noise suppression. Experimental results showcase the filter's performance across various noise scenarios, including different noise variances and signal-to-noise ratios. The testing framework evaluates filtering effectiveness through quantitative metrics like PSNR (Peak Signal-to-Noise Ratio) and qualitative visual assessments of processed images. By studying the source code and experimental data, researchers can gain deep insights into PCNN-based filtering algorithms' operational principles and practical applications. The program serves as a valuable reference for further research and development in neural network-based signal processing, offering a robust foundation for algorithm optimization and extension to other noise types. The code structure facilitates modular customization, allowing researchers to modify neuron dynamics, connection weights, or integration with other pre-processing/post-processing techniques.