  Restoring images distorted by atmospheric turbulence is a ubiquitous problem
in long-range imaging applications. While existing deep-learning-based methods
have demonstrated promising results in specific testing conditions, they suffer
from three limitations: (1) lack of generalization capability from synthetic
training data to real turbulence data; (2) failure to scale, hence causing
memory and speed challenges when extending the idea to a large number of
frames; (3) lack of a fast and accurate simulator to generate data for training
neural networks. In this paper, we introduce the turbulence mitigation
transformer (TMT) that explicitly addresses these issues. TMT brings three
contributions: Firstly, TMT explicitly uses turbulence physics by decoupling
the turbulence degradation and introducing a multi-scale loss for removing
distortion, thus improving effectiveness. Secondly, TMT presents a new
attention module along the temporal axis to extract extra features efficiently,
thus improving memory and speed. Thirdly, TMT introduces a new simulator based
on the Fourier sampler, temporal correlation, and flexible kernel size, thus
improving our capability to synthesize better training data. TMT outperforms
state-of-the-art video restoration models, especially in generalizing from
synthetic to real turbulence data. Code, videos, and datasets are available at
\href{https://xg416.github.io/TMT}{https://xg416.github.io/TMT}.
