Tackling image degradation due to atmospheric turbulence, particularly in
dynamic environment, remains a challenge for long-range imaging systems.
Existing techniques have been primarily designed for static scenes or scenes
with small motion. This paper presents the first segment-then-restore pipeline
for restoring the videos of dynamic scenes in turbulent environment. We
leverage mean optical flow with an unsupervised motion segmentation method to
separate dynamic and static scene components prior to restoration. After camera
shake compensation and segmentation, we introduce foreground/background
enhancement leveraging the statistics of turbulence strength and a transformer
model trained on a novel noise-based procedural turbulence generator for fast
dataset augmentation. Benchmarked against existing restoration methods, our
approach restores most of the geometric distortion and enhances sharpness for
videos. We make our code, simulator, and data publicly available to advance the
field of video restoration from turbulence: riponcs.github.io/TurbSegRes

本文介绍了一种用于恢复湍流环境中动态场景视频的首个先分割再恢复的流程。我们利用均值光流和无监督的运动分割方法，在恢复之前对动态和静态场景组件进行分离。经过相机抖动补偿和分割后，我们利用湍流强度统计和基于噪声的程序化湍流生成器上训练的转换模型进行前景 / 背景增强，以实现快速数据集增强。与现有的恢复方法相比，在视频中恢复了大部分几何失真并提高了清晰度。我们公开提供我们的代码、模拟器和数据，以推进从湍流中恢复视频的领域：ripponcs.github.io/TurbSegRes。

Turb-Seg-Res：用于大气湍流动态视频的分割 - 恢复流程

Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with  Atmospheric Turbulence

Atmospheric turbulence can significantly degrade the quality of images
acquired by long-range imaging systems by causing spatially and temporally
random fluctuations in the index of refraction of the atmosphere. Variations in
the refractive index causes the captured images to be geometrically distorted
and blurry. Hence, it is important to compensate for the visual degradation in
images caused by atmospheric turbulence. In this paper, we propose a deep
learning-based approach for restring a single image degraded by atmospheric
turbulence. We make use of the epistemic uncertainty based on Monte Carlo
dropouts to capture regions in the image where the network is having hard time
restoring. The estimated uncertainty maps are then used to guide the network to
obtain the restored image. Extensive experiments are conducted on synthetic and
real images to show the significance of the proposed work. Code is available at
: this https URL

本文提出了基于深度学习的方法，利用蒙特卡罗实验退出的认知不确定性来恢复大气湍流影响下的退化单张图像，并利用估计的不确定性图来指导网络获取恢复图像。