MODULE: A Mutual-Promoting Deep Unfolding Framework Towards Degradation-Robust Multi-modal Image Fusion

Multi-modal image fusion is crucial for comprehensive scene representation, yet real-world degradations compromise its efficacy, necessitating degradation-robust fusion paradigms. Existing degradation-robust multi-modal image fusion methods are often hindered by cascaded sub-optimal solution or black-box opacity. To address these challenges, we propose a theory-inspired mutual-promoting deep unfolding framework. It reformulates degradation-robust image fusion as a joint optimization problem, utilizing the degradation model and modality generation mechanism to explicitly model both single-task priors and cross-task dependencies. By decomposing the complex multi-task optimization problem into task-related iterative subproblems, the framework establishes a bidirectional reciprocal information flow between restoration and fusion. This process is further unfolded into a multi-stage deep neural network, where each component explicitly corresponds to a specific mathematical operation. In our framework, the fused cross-modal prior actively regularizes the ill-posed restoration, while the purified modality features continuously refine the fusion output. It ensures a transparent architecture that combines the merits of both model-based and data-driven methodologies. Extensive experiments demonstrate that our paradigm achieves state-of-the-art performance while providing superior interpretability.