Adaptive Prompting-Driven Degradation-Aware Fusion for Infrared and Visible Images

Infrared and visible image fusion aims to combine the complementary advantages of thermal radiation information and rich texture details to generate more informative images for downstream perception tasks. However, existing deep learning-based methods usually assume ideal imaging conditions and often suffer from performance degradation in complex environments such as low illumination, rain interference, and strong lighting disturbances. To address this problem, this paper proposes an adaptive prompting-driven degradation-aware fusion framework. Specifically, a degradation-aware prompt generation module is introduced to automatically perceive degradation patterns from the input images and generate structured conditional prompts. These prompts guide the network to adaptively adjust feature representations through learnable affine modulation. Furthermore, a semantic-aligned feature learning strategy is designed to ensure consistent cross-modal representation in the latent space. Extensive experiments demonstrate that the proposed method achieves superior performance compared with several state-of-the-art fusion approaches under both normal and degraded conditions.