Spatio-Temporal Turbulence Mitigation: A Translational Perspective

Abstract

Recovering images distorted by atmospheric turbulence is a challenging inverse problem due to the stochastic nature of turbulence. Although numerous turbulence mitigation (TM) algorithms have been proposed their efficiency and generalization to real-world dynamic scenarios remain severely limited. Building upon the intuitions of classical TM algorithms we present the Deep Atmospheric TUrbulence Mitigation network (DATUM). DATUM aims to overcome major challenges when transitioning from classical to deep learning approaches. By carefully integrating the merits of classical multi-frame TM methods into a deep network structure we demonstrate that DATUM can efficiently perform long-range temporal aggregation using a recurrent fashion while deformable attention and temporal-channel attention seamlessly facilitate pixel registration and lucky imaging. With additional supervision tilt and blur degradation can be jointly mitigated. These inductive biases empower DATUM to significantly outperform existing methods while delivering a tenfold increase in processing speed. A large-scale training dataset ATSyn is presented as a co-invention to enable the generalization to real turbulence. Our code and datasets are available at https://xg416.github.io/DATUM/