Learning Geometry-Stabilized Intermediate Representations for Real-World Atmospheric Turbulence Restoration

Atmospheric turbulence introduces geometric distortions and spatially varying blur, making it challenging to restore images captured over long distances.Although learning-based methods have shown promising results, their generalization to real-world scenes is often limited by reliance on synthetic training data that may not fully capture actual imaging conditions. Motivated by the geometry-stabilizing effect of temporal integration, we introduce a geometry-stabilized intermediate representation that can be learned directly from real turbulent videos while retaining residual blur. This representation enables a short-to-representation-to-clean framework that decouples single-frame restoration into geometric stabilization and detail restoration, with the latter learned from physics-guided blurred/clean pairs. We further propose a training-free algorithm that registers and refines the intermediate representations, extending the framework to a flexible number of input frames while keeping the networks fixed. Across five real-world benchmarks, our model achieves strong performance in perceptual quality, image sharpness, temporal stability, and downstream text recognition. Our code will be publicly released.