tmospheric turbulence presents a significant challenge in long-range imaging.
Current restoration algorithms often struggle with temporal inconsistency, as
well as limited generalization ability across varying turbulence levels and
scene content different than the training data. To tackle these issues, we
introduce a self-supervised method, Consistent Video Restoration through
Turbulence (ConVRT) a test-time optimization method featuring a neural video
representation designed to enhance temporal consistency in restoration. A key
innovation of ConVRT is the integration of a pretrained vision-language model
(CLIP) for semantic-oriented supervision, which steers the restoration towards
sharp, photorealistic images in the CLIP latent space. We further develop a
principled selection strategy of text prompts, based on their statistical
correlation with a perceptual metric. ConVRT's test-time optimization allows it
to adapt to a wide range of real-world turbulence conditions, effectively
leveraging the insights gained from pre-trained models on simulated data.
ConVRT offers a comprehensive and effective solution for mitigating real-world
turbulence in dynamic videos.

通过在 CLIP 潜空间中优化自监督方法 ConVRT，结合真实世界湍流条件下的训练模型，提供了一个有效的解决方案来减轻动态视频中的大气湍流挑战。

ConVRT: 在测试时间优化神经视频表征间通过湍流的连贯视频修复

ConVRT: Consistent Video Restoration Through Turbulence with Test-time  Optimization of Neural Video Representations

Complex spatial and temporal structures are inherent characteristics of
turbulent fluid flows and comprehending them poses a major challenge. This
comprehesion necessitates an understanding of the space of turbulent fluid flow
configurations. We employ a diffusion-based generative model to learn the
distribution of turbulent vorticity profiles and generate snapshots of
turbulent solutions to the incompressible Navier-Stokes equations. We consider
the inverse cascade in two spatial dimensions and generate diverse turbulent
solutions that differ from those in the training dataset. We analyze the
statistical scaling properties of the new turbulent profiles, calculate their
structure functions, energy power spectrum, velocity probability distribution
function and moments of local energy dissipation. All the learnt scaling
exponents are consistent with the expected Kolmogorov scaling and have lower
errors than the training ones. This agreement with established turbulence
characteristics provides strong evidence of the model's capability to capture
essential features of real-world turbulence.

利用基于扩散的生成模型学习湍流涡度轮廓的分布，生成与训练数据集不同的多样化湍流解，并分析新湍流轮廓的统计缩放特性、能量功率谱、速度概率分布函数和局部能量耗散矩。通过与已建立的湍流特性的一致性，该模型证明了其捕捉实际湍流关键特征的能力。