Adaptive PCNN (Pulse-Coupled Neural Network) Fusion

In this paper, we implement an adaptive PCNN (Pulse-Coupled Neural Network) fusion method that plays a significant role in image fusion applications. This approach achieves pixel-level fusion through neural synchronization mechanisms, where the PCNN model automatically adjusts linking strength parameters based on image characteristics. The implementation involves feeding input images into the neural network, where neurons corresponding to pixel locations fire synchronously based on intensity similarity and spatial proximity. This method demonstrates particular effectiveness for fusing visible light and infrared images by preserving thermal signatures from infrared data while maintaining visual details from optical images. The adaptive mechanism optimizes fusion weights dynamically through entropy maximization or gradient-based criteria, ensuring balanced information transfer between source images. By leveraging this fusion technique, we can comprehensively utilize information from different image sources to obtain richer and more accurate composite results. Therefore, the adaptive PCNN fusion method serves as a highly effective image fusion technology applicable across various domains including military systems, medical imaging, and environmental monitoring.