Toward a Training-Free Plug-and-Play Refinement Framework for Infrared and Visible Image Registration and Fusion

Infrared and Visible Image Fusion (IVIF) under unregistered conditions has been of great interest in various visual tasks under challenging environments. While existing approaches often demonstrate promising results on specific benchmarks, they tend to exhibit performance drops in unseen scenarios and incur high computational overhead when retrained on new datasets. To address these challenges, we propose TRACE, a Training-free Reinforcement-based Alignment method for Cross-modality Enhancement, which incorporates Evaluator, a rewarding network, into an evaluation-driven Reinforcement Learning (RL) framework, enabling efficient and plug-and-play refinement of any existing registration approach. Specifically, TRACE constructs the Evaluator network to assess the alignment quality of the given registration model, generating confidence scores and adjustment masks via spatial and channel attention. Leveraging these cues as RL rewards, TRACE iteratively refines the registration network to mitigate misalignments until the accumulated improvement is satisfied. Due to its training-free and plug-and-play nature, TRACE notably enhances fusion results across diverse and unseen scenarios. TRACE achieves impressive improvements in different methods across diverse datasets with minimal computational cost. The project page is available at https://github.com/pubyLu/TRACE.