Unsupervised Region-Growing Network for Object Segmentation in
  Atmospheric Turbulence
Dehao Qin Ripon Saha Suren Jayasuriya Jinwei Ye Nianyi Li
In this paper, we present a two-stage unsupervised foreground object
segmentation network tailored for dynamic scenes affected by atmospheric
turbulence. In the first stage, we utilize averaged optical flow from
turbulence-distorted image sequences to feed a novel region-growing algorithm,
crafting preliminary masks for each moving object in the video. In the second
stage, we employ a U-Net architecture with consistency and grouping losses to
further refine these masks optimizing their spatio-temporal alignment. Our
approach does not require labeled training data and works across varied
turbulence strengths for long-range video. Furthermore, we release the first
moving object segmentation dataset of turbulence-affected videos, complete with
manually annotated ground truth masks. Our method, evaluated on this new
dataset, demonstrates superior segmentation accuracy and robustness as compared
to current state-of-the-art unsupervised methods.