A generative infrared and visible image fusion method inspired by human cognitive laws combines a multi-scale variational bottleneck encoder and a diffusion model guided by physical laws to achieve superior structural consistency and detail quality.

We propose DAA, a trainable module that enables real-time adaptation by amplifying feature-level discrepancies between known and unknown classes in TTD.