Physically-Grounded Turbulence Mitigation with Frame-Shared Degradation Parameters

Atmospheric turbulence severely degrades long-range images with distortions and blur, hindering downstream applications. While supervised methods rely on synthetic data with limited real-world generalization, existing unsupervised approaches often ignore the underlying physics, leading to suboptimal restoration. We propose TMFS, an optimization-based and physically-grounded approach for unsupervised turbulence mitigation. The method operates by optimizing an imaging model with frame-shared degradation parameters under physically-motivated regularization. Inspired by sampling procedures in physical simulators, the degradation parameters are further decomposed into a frame-shared correlation function and per-frame noise maps. TMFS gains a strong inductive bias that improves generalization and mitigates overfitting. In extensive experiments, TMFS achieves state-of-the-art results among unsupervised methods. In contrast, supervised methods show a significant domain gap on real data, thereby validating the advantage of our physics-aware, unsupervised approach.