Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence
arxiv(2024)
摘要
Tackling image degradation due to atmospheric turbulence, particularly in
dynamic environment, remains a challenge for long-range imaging systems.
Existing techniques have been primarily designed for static scenes or scenes
with small motion. This paper presents the first segment-then-restore pipeline
for restoring the videos of dynamic scenes in turbulent environment. We
leverage mean optical flow with an unsupervised motion segmentation method to
separate dynamic and static scene components prior to restoration. After camera
shake compensation and segmentation, we introduce foreground/background
enhancement leveraging the statistics of turbulence strength and a transformer
model trained on a novel noise-based procedural turbulence generator for fast
dataset augmentation. Benchmarked against existing restoration methods, our
approach restores most of the geometric distortion and enhances sharpness for
videos. We make our code, simulator, and data publicly available to advance the
field of video restoration from turbulence: riponcs.github.io/TurbSegRes
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