Data-Driven Bayesian Maximum Entropy Multi-Objective Hyperparameter Optimization for PCNN Image Fusion

To address the challenges of hyperparameter tuning and reliance on manual experience in dual-channel pulse-coupled neural networks (DCPCNN) for image fusion tasks, the paper proposes a data-driven Bayesian maximum entropy (DBME) multi-objective optimization method to enhance image fusion performance. Firstly, the source images are transformed into the NSST domain with low-frequency bands and high-frequency bands. Secondly, we design a DCPCNN model based on DBME optimization to fuse the high-frequency subbands. For the low-frequency subbands, a method based on weighted local energy and multi-scale morphological gradient fusion rule is proposed. Finally, the fused image is reconstructed by the NSST inverse transform. The results demonstrate that, compared to other fusion methods, this approach outperforms in the fusion of infrared and visible light images, as well as multi-focus images, with significant advantages in metrics such as AG, SD, SCD, and VIFF.