G2Fusion: Geometric-to-Generative Image Fusion via Registration-Restoration Evolution

Real-world multi-modal image fusion faces two fundamental challenges: spatial misalignment and complex degradations induced by heterogeneous imaging sensors. These factors are intrinsically coupled, but most existing fusion methods typically address only one of them, inevitably leading to failure under the other challenge. In this paper, we propose G2Fusion, the first fusion framework that simultaneously addresses image registration and information restoration. By developing a novel geometric-to-generative paradigm, it can directly produce high-quality fused images from unregistered and degraded inputs captured by heterogeneous imaging sensors. This framework consists of two key modules: a flow-based geometric deformation reduction module (Flow-GDR) and a DiT-based generative fusion module (DiT-GF). The former reduces large non-rigid discrepancies through dense flow estimation. The latter performs generative fusion, progressively refining residual misalignments while restoring degraded content through iterative denoising. To enable effective interaction among registration, restoration, and fusion, we design two complementary mechanisms. On the one hand, a target distribution mining strategy is introduced to construct a joint objective distribution from registration, restoration, and fusion, effectively guiding the optimization of DiT-GF. On the other hand, we develop a mutual promotion mechanism that establishes a closed-loop interaction between Flow-GDR and DiT-GF by re-estimating the residual deformation between the fused output and the infrared reference. Extensive experiments demonstrate that G2Fusion consistently outperforms state-of-the-art methods in terms of registration, restoration, and fusion.