LTOFusion: A Learning-to-Optimize Framework With Flow Matching for Unsupervised Image Fusion

Multimodal Image Fusion (MMIF) aims to synthesize complementary information from different modalities to generate comprehensive fused images, thereby facilitating downstream applications. Existing methods typically employ deep neural networks to directly construct high-dimensional image-to-image mappings, which is highly challenging, struggling to extract generalizable patterns for various fusion scenarios. Inspired by meta learning, we propose a learning-to-optimize fusion framework, named LTOFusion, which formulates image fusion as a trajectory optimization problem, decoupling the complicated fusion problem into multistage subproblems. Subsequently, a restricted state transition function based on flow matching is designed to compress the prediction space and lead the network to build an image-to-flow mapping and fine-tune the current fusion state. To facilitate model training, we collect intermediate fusion states and utilize a memory-replay strategy, further enhancing the sample diversity and model robustness. In addition, a hybrid loss with respect to intensity, gradient, structure, and local normalized cross-correlation is designed to improve image details and reduce potential artifacts for fusion results. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance across multiple fusion tasks and downstream applications without requiring fine-tuning. The code is available at https://github.com/HeDan-11/LTOFusion.