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
will be available at
\href{https://xg416.github.io/DATUM}{\textcolor{pink}{this https URL}}

利用经典图像修复算法的直觉，我们提出了深度大气湍流抑制网络（DATUM），能够在深度学习方法中高效地执行长距离时序聚合，同时无缝地促进像素注册和幸运成像，并通过额外的监督来共同减轻倾斜和模糊退化，从而显著优于现有方法，并提供十倍的处理速度提升。