Multi-Source Image Fusion Using Dual-Channel PCNN

Multi-source image fusion is achieved using a dual-channel Pulse-Coupled Neural Network (PCNN), which presents a straightforward and easily implementable algorithmic approach. The dual-channel PCNN architecture enables simultaneous processing of images from multiple sources, merging them into a more comprehensive composite image. This method requires implementing parallel input channels for handling different image sources, with synchronization mechanisms to ensure temporal alignment of neuronal firing patterns. Key implementation aspects include setting appropriate linking parameters and decay constants to control pulse synchronization between channels.In image processing applications, this technique demonstrates broad utility and delivers superior image quality with enhanced detail preservation. The algorithm typically involves preprocessing steps like image registration, followed by PCNN-based fusion using pixel-level linking and pulse-coupled mechanisms. Therefore, utilizing dual-channel PCNN for multi-source image fusion represents a highly effective and feasible methodology that can be implemented with basic neural network operations and minimal computational overhead.