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arXiv · 2508.10582

EvTurb: Event Camera Guided Turbulence Removal

Yixing Liu·Minggui Teng·Yifei Xia·Peiqi Duan·Boxin Shi
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Abstract

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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Yixing Liu, Minggui Teng, Yifei Xia, Peiqi Duan, Boxin Shi. 2025-08-14. EvTurb: Event Camera Guided Turbulence Removal. https://arxiv.org/abs/2508.10582

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