Abstract
Turbulence mitigation (TM) is highly ill-posed due to the stochastic nature of atmospheric turbulence. Most methods rely on multiple frames recorded by conventional cameras to capture stable patterns in natural scenarios. However, they inevitably suffer from a trade-off between accuracy and efficiency: more frames enhance restoration at the cost of higher system latency and larger data overhead. Event cameras, equipped with microsecond temporal resolution and efficient sensing of dynamic changes, offer an opportunity to break the bottleneck. In this work, we present EHETM, a high-quality and efficient TM method inspired by the superiority of events to model motions in continuous sequences. We discover two key phenomena: (1) turbulence-induced events exhibit distinct polarity alternation correlated with sharp image gradients, providing structural cues for restoring scenes; and (2) dynamic objects form spatiotemporally coherent ``event tubes'' in contrast to irregular patterns within turbulent events, providing motion priors for disentangling objects from turbulence. Based on these insights, we design two complementary modules that respectively leverage polarity-weighted gradients for scene refinement and event-tube constraints for motion decoupling, achieving high-quality restoration with few frames. Furthermore, we construct two real-world event-frame turbulence datasets covering atmospheric and thermal cases. Experiments show that EHETM outperforms SOTA methods, especially under scenes with dynamic objects, while reducing data overhead and system latency by approximately 77.3% and 89.5%, respectively. Our code is available at: this https URL.
Abstract (translated)
湍流缓解(TM) 因其随机性而具有高度不适定性。多数方法依赖传统相机拍摄的多帧图像来捕捉自然场景中的稳定模式，但不可避免地面临精度与效率的权衡：增加帧数虽能提升复原质量，却会带来更高的系统延迟与数据开销。事件相机具备微秒级时间分辨率并能高效感知动态变化，为突破这一瓶颈提供了可能。本研究提出 EHETM，一种受事件在连续序列中建模运动优势启发的高质量高效 TM 方法。我们发现两个关键现象：(1) 湍流引发的事件呈现与清晰图像梯度相关的明显极性交替，为场景复原提供结构线索；(2) 动态物体形成时空连贯的“事件管”，与湍流事件中的不规则模式形成对比，为分离物体与湍流提供运动先验。基于这些洞察，我们设计了两个互补模块：分别利用极性加权梯度进行场景细化，以及通过事件管约束实现运动解耦，从而以少量帧实现高质量复原。此外，我们构建了两个涵盖大气与热辐射场景的真实世界事件-帧湍流数据集。实验表明，EHETM 优于当前最优方法，尤其在动态物体场景中表现突出，同时将数据开销与系统延迟分别降低约 77.3% 和 89.5%。代码已公开：this https URL。
URL
https://arxiv.org/abs/2603.20708
