用事件相机数据分离湍流模糊和扭曲，提升图像质量。
EvTurb: Event Camera Guided Turbulence Removal
- 通过事件流建模湍流形成，分两步分离模糊与倾斜
- 在真实数据集上实现更优去湍流效果且计算高效
- 适合需要高精度视觉感知的户外成像场景
大气湍流会引入模糊和几何倾斜畸变，严重损害图像质量，给下游计算机视觉任务带来挑战。现有单帧和多帧方法因湍流畸变的复合性而难以解决该高度病态问题。为此，我们提出 EvTurb，一种利用高速事件流解耦模糊与倾斜效应的湍流去除框架。EvTurb 通过建模事件式湍流生成过程，采用新颖的两阶段事件引导网络：首先使用事件积分降低粗略输出中的模糊；随后利用原始事件流生成的方差图消除精细化输出中的倾斜畸变。此外，我们构建了首个真实采集的湍流数据集 TurbEvent，涵盖多种湍流场景。实验表明，EvTurb 在保持计算效率的同时超越现有最优方法。
原文摘要 · Abstract (English)
Atmospheric turbulence degrades image quality by introducing blur and geometric tilt distortions, posing significant challenges to downstream computer vision tasks. Existing single-image and multi-frame methods struggle with the highly ill-posed nature of this problem due to the compositional complexity of turbulence-induced distortions. To address this, we propose EvTurb, an event guided turbulence removal framework that leverages high-speed event streams to decouple blur and tilt effects. EvTurb decouples blur and tilt effects by modeling event-based turbulence formation, specifically through a novel two-step event-guided network: event integrals are first employed to reduce blur in the coarse outputs. This is followed by employing a variance map, derived from raw event streams, to eliminate the tilt distortion for the refined outputs. Additionally, we present TurbEvent, the first real-captured dataset featuring diverse turbulence scenarios. Experimental results demonstrate that EvTurb surpasses state-of-the-art methods while maintaining computational efficiency.
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