  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 generalization in real turbulence. Our code and datasets
are available at https://xg416.github.io/DATUM.
