When Motion Misleads: Causal-Role-Aware Motion-Turbulence Disentanglement for Turbulent Infrared Video Super-Resolution

Infrared turbulent video super-resolution is fundamentally challenging due to the entanglement of real object/camera motion and random turbulence-induced distortions. Existing turbulence mitigation and video super-resolution methods often conflate turbulence-induced pseudo-motion with real object/camera motion and model them as a unified temporal correspondence signal, leading to spurious alignments and unstable details. We propose CAMO, a Causal-role-Aware Motion-turbulence disentanglement framework for One-step diffusion restoration, rooted in the physical principle that the real motion induces coherent phase transport in the infrared domain, while turbulence-induced refractive perturbations break such coherence. This coherence provides a reliability cue for factorizing mixed temporal dynamics into constructive object motion and destructive turbulence components. The turbulence component is used to suppress local non-rigid distortions, while the real motion component promotes temporal consistency and enhances motion-aware details during reconstruction. Furthermore, CAMO regulates the generative conditioning and velocity prediction based on motion reliability, enabling constructive motion cues to be selectively exploited while preventing the amplification of uncertain turbulence-induced pseudo-motion. This unified design enables end-to-end turbulence suppression, temporal information utilization, and high-resolution reconstruction. Extensive experiments under complex infrared turbulence demonstrate improved restoration fidelity, temporal stability, and robustness over existing methods. Anonymous code is available at https://camo23333.github.io/camo.github.io/CAMO.