ResFusion: Medical Image Fusion Driven by Implicit-Forward Diffusion and Time-aware Joint Optimization

Standard training of diffusion models typically relies on a forward diffusion process anchored by ground-truth target images. However, in image fusion tasks, the scarcity of real fused images makes it difficult to formulate a forward process, thereby precluding the standard training. Fortunately, our visual analysis reveals that image fusion can be effectively modeled without standard training or reliance on the real images. Nevertheless, the misalignment of input image pairs remains a significant bottleneck for fusion quality. Recognizing that one-off registration is ill-suited to the progressive generation of diffusion models, we propose a unified registration-fusion framework driven by a time-aware joint optimization mechanism. Specifically, we design a time-aware registration network to progressively optimize registration to guide fusion, while the fusion results provide feedback constraints to the registration network at each time step. This mechanism facilitates joint optimization of registration and fusion, significantly improving the quality of fused results. Experimental results show that the proposed method performs excellently on multiple datasets, validating its effectiveness and superiority.