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Restoring images distorted by atmospheric turbulence is a ubiquitous problemin long-range imaging applications. While existing deep-learning-based methodshave demonstrated promising results in specific testing conditions, they sufferfrom three limitations: (1) lack of generalization capability from synthetictraining data to real turbulence data; (2) failure to scale, hence causingmemory and speed challenges when extending the idea to a large number offrames; (3) lack of a fast and accurate simulator to generate data for trainingneural networks. In this paper, we introduce the turbulence mitigationtransformer (TMT) that explicitly addresses these issues. TMT brings threecontributions: Firstly, TMT explicitly uses turbulence physics by decouplingthe turbulence degradation and introducing a multi-scale loss for removingdistortion, thus improving effectiveness. Secondly, TMT presents a newattention module along the temporal axis to extract extra features efficiently,thus improving memory and speed. Thirdly, TMT introduces a new simulator basedon the Fourier sampler, temporal correlation, and flexible kernel size, thusimproving our capability to synthesize better training data. TMT outperformsstate-of-the-art video restoration models, especially in generalizing fromsynthetic to real turbulence data. Code, videos, and datasets are available at\href{https://xg416.github.io/TMT}{https://xg416.github.io/TMT}.

Imaging through the Atmosphere using Turbulence Mitigation Transformer