RMFAT: Recurrent Multi-scale Feature Atmospheric Turbulence Mitigator
This addresses the challenge of real-time video restoration for resource-constrained scenarios, though it is incremental as it builds on existing methods by optimizing efficiency.
The paper tackled the problem of atmospheric turbulence degrading video quality by proposing RMFAT, a lightweight recurrent framework that restores video frames with improved clarity (nearly 9% SSIM gain) and inference speed (over fourfold runtime reduction).
Atmospheric turbulence severely degrades video quality by introducing distortions such as geometric warping, blur, and temporal flickering, posing significant challenges to both visual clarity and temporal consistency. Current state-of-the-art methods are based on transformer and 3D architectures and require multi-frame input, but their large computational cost and memory usage limit real-time deployment, especially in resource-constrained scenarios. In this work, we propose RMFAT: Recurrent Multi-scale Feature Atmospheric Turbulence Mitigator, designed for efficient and temporally consistent video restoration under AT conditions. RMFAT adopts a lightweight recurrent framework that restores each frame using only two inputs at a time, significantly reducing temporal window size and computational burden. It further integrates multi-scale feature encoding and decoding with temporal warping modules at both encoder and decoder stages to enhance spatial detail and temporal coherence. Extensive experiments on synthetic and real-world atmospheric turbulence datasets demonstrate that RMFAT not only outperforms existing methods in terms of clarity restoration (with nearly a 9\% improvement in SSIM) but also achieves significantly improved inference speed (more than a fourfold reduction in runtime), making it particularly suitable for real-time atmospheric turbulence suppression tasks.