PTFusion: Photometric and Texture Prior Learning for Illumination-Adaptive Infrared and Visible Image Fusion

The fusion of infrared and visible images in low-light environments faces the problem of degraded photometric and texture information. The existing methods usually couple photometric restoration with texture compensation, which limits their adaptability to different lighting conditions. To address these issues, we propose PTFusion, a multi-illumination image fusion method based on photometric and texture priors. Specifically, we use images under multiple lighting conditions to learn photometric priors, mapping different lighting states to a unified photometric representation space to enhance the model's adaptability to different lighting conditions. At the same time, we construct a texture prior network to inject texture features into the fusion feature space to compensate for the lack of texture information and insufficient local structural details caused by lighting degradation. In the fusion process, we use photometric priors to adapt visible features and inject texture priors to compensate for lighting degradation and texture deficiencies. Then, prior of photometric and texture is used to supervise the fusion results, improving the photometric fidelity and texture details of the fused image. The experimental results show that our method has excellent multi-light adaptation ability and exhibits competitive comprehensive performance in fusion tasks and downstream visual tasks. The code will be released publicly.