Position-surpassing Flow Estimator: Advanced Graph-Based Motion Reconstruction in the Dark

Dark optical flow estimation aims to predict pixel-wise displacement between consecutive noisy dark frames. Existing methods primarily focus on enhancing feature-specific representations before cross-image matching, with few attention devoted to the inherent dark degradation during flow decoding for achieving holistic motion understanding of a given dark scene. In this paper, we introduce the Position-surpassing Flow Estimator (PsFE), which integrates a global graph method into flow decoders to accentuate holistic motion discrimination and robustness. In detail, we incorporate a graph-based motion reconstruction into the decoding paradigm to adaptive aggregate motion-rich feature channels and suppress degraded ones from a more global view. This characteristic suppression retains the graph structure, which is a robust characteristic in the dark. To accurately encode long-range pixel connections, PsFE employs a novel masked global encoder to capture top-kimportant features by using a sparse masking strategy and dynamic inductive modulation that suppresses noise and interference that only exist under dark conditions. Experiments on challenging FCDN and VBOF benchmarks demonstrate the effectiveness of our PsFE with superior performance over advanced methods.