Infrared and visible image fusion aims to synthesize images with thermal radiation and rich texture details. Existing methods suffer from inadequate feature representation and imbalanced fusion due to cross-modal discrepancies, leading to blurred details or insufficient thermal saliency. To address this, we propose DiffFuseNet, which integrates diffusion-guided feature enhancement and explicit feature decoupling. The Dual Diffusion-based Feature Enhancement (D 2 FE) module enhances cross-modal robustness via controllable noise injection and denoising. The Explicit Decoupling and Frequency Decomposition (EDFD) module separates features into shared, modality-specific, and frequency-aware components. Coupled with a frequency-aware fusion mechanism using Haar wavelet and invertible neural networks and a two-stage training strategy, our approach achieves balanced information integration. Experiments on M3FD, RoadScene, and TNO datasets show that DiffFuseNet outperforms state-of-the-art methods in visual quality and objective metrics, with superior detail preservation, thermal saliency, and structural consistency.
Wang et al. (Mon,) studied this question.