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。

时空湍流抑制：一个翻译视角

Spatio-Temporal Turbulence Mitigation: A Translational Perspective

The atmospheric and water turbulence mitigation problems have emerged as
challenging inverse problems in computer vision and optics communities over the
years. However, current methods either rely heavily on the quality of the
training dataset or fail to generalize over various scenarios, such as static
scenes, dynamic scenes, and text reconstructions. We propose a general implicit
neural representation for unsupervised atmospheric and water turbulence
mitigation (NeRT). NeRT leverages the implicit neural representations and the
physically correct tilt-then-blur turbulence model to reconstruct the clean,
undistorted image, given only dozens of distorted input images. Moreover, we
show that NeRT outperforms the state-of-the-art through various qualitative and
quantitative evaluations of atmospheric and water turbulence datasets.
Furthermore, we demonstrate the ability of NeRT to eliminate uncontrolled
turbulence from real-world environments. Lastly, we incorporate NeRT into
continuously captured video sequences and demonstrate $48 \times$ speedup.

提出了一种基于隐式神经表示的无监督大气和水湍流抑制方法（NeRT），通过利用隐式神经表示和物理正确的倾斜 - 模糊湍流模型，仅仅依靠数十个扭曲输入图像就能重建出干净、无扭曲的图像，并且在大气和水湍流数据集的各种定性和定量评估中表现优于现有技术，同时展示了 NeRT 消除现实环境中的非受控湍流的能力，并将 NeRT 融入连续捕获的视频序列中实现了 48 倍的加速。