Hybrid Reinforcement Learning for One-step Degraded Infrared and Visible Image Fusion

Infrared-Visible image fusion (IVIF) is essential for robust perception, yet existing methods remain limited under complex degradations. While diffusion models (DMs) offer powerful modeling capabilities to combat such degradations, their iterative process incurs high computational cost. One-step distillation effectively accelerates inference but typically leads to over-smoothed results. To resolve these limitations, we propose a one-step fusion framework with hybrid reinforcement learning, termed OneHRL. We treat degraded inputs as intermediate noisy states along a generative trajectory, enabling a distilled one-step generator to rectify them directly to the clean fusion results. Concurrently, to mitigate the over-smoothed results in distillation, we incorporate the Flow-GRPO paradigm to introduce stochasticity into the deterministic one-step generation. This restores the stochastic exploration necessary for sampling-based reinforcement learning. Furthermore, we design a coarse-to-fine reward that integrates a global semantic reward with an object-level reward. This hybrid reward ensures that the generator captures both naturalness and fine-grained texture. Extensive experiments demonstrate that our method achieves superior fusion quality and efficiency.