Atmospheric turbulence poses a challenge for the interpretation and visual perception of visual imagery due to its distortion effects. Model-based approaches have been used to address this, but such methods often suffer from artefacts associated with moving content. Conversely, deep learning based methods are dependent on large and diverse datasets that may not effectively represent any specific content. In this paper, we address these problems with a self-supervised learning method that does not require ground truth. The proposed method is not dependent on any dataset outside of the single data sequence being processed but is also able to improve the quality of any input raw sequences or pre-processed sequences. Specifically, our method is based on an accelerated Deep Image Prior (DIP), but integrates temporal information using pixel shuffling and a temporal sliding window. This efficiently learns spatio-temporal priors leading to a system that effectively mitigates atmospheric turbulence distortions. The experiments show that our method improves visual quality results qualitatively and quantitatively.

翻译：大气湍流因其畸变效应对图像解译和视觉感知构成挑战。基于模型的传统方法虽被用于解决该问题，却常常因运动内容而产生伪影。相反，基于深度学习的方法依赖于大规模、多样化的数据集，但这些数据可能无法有效表征特定场景内容。本文提出一种无需真实标签的自监督学习方法来解决上述问题。该方法既不依赖于除当前处理序列外的任何外部数据集，又能提升原始序列或预处理序列的质量。具体而言，我们的方法基于加速的深度图像先验（DIP），通过像素混洗和时序滑动窗口融合时间信息。该机制高效学习时空先验，从而构建有效缓解大气湍流畸变的系统。实验结果表明，本方法在视觉质量上均取得了定性及定量的显著提升。