Pulse-Coupled Neural Network Denoising Algorithm

In this paper, we employ the denoising algorithm based on pulse-coupled neural networks (PCNN) to enhance signal quality. This algorithm demonstrates remarkable effectiveness in noise suppression, resulting in cleaner and more reliable signals. The implementation typically involves neuron firing synchronization mechanisms where pixels or signal samples with similar characteristics trigger simultaneous pulses, effectively separating noise components from meaningful signals. Key algorithmic steps include: - Setting dynamic linking parameters to control pulse transmission between neurons - Implementing iterative pulse coupling to capture signal correlations - Using pulse synchronization to identify and preserve signal patterns - Applying threshold mechanisms to filter out asynchronous noise components Through this PCNN-based approach, we can significantly improve system performance and achieve superior signal processing outcomes. Notably, the algorithm exhibits outstanding performance across various signal types including audio signals, image data, and sensor readings. The method's adaptability to different noise characteristics makes it particularly valuable for practical applications. Therefore, we confidently recommend the pulse-coupled neural network denoising algorithm for enhancing system performance in diverse signal processing scenarios.