FluidRain: Divergence-Free Rain Flow as an Attention Bias for Loop-in-Loop Video Deraining

Existing video deraining methods typically exploit neighbouring frames through either explicit alignment or implicit spatiotemporal aggregation. Explicit alignment relies on accurate motion estimation, which can become unreliable under dense rain, while implicit aggregation avoids alignment but lacks explicit guidance on the directional and temporally coherent structure of rain. This leaves a gap between reliable temporal aggregation and explicit modeling of rain motion. We propose FluidRain, a parameter-efficient video derainer that uses divergence-free rain flow to guide Loop-in-Loop attention across scales and neighbouring frames. Motivated by fluid mechanics, FluidRain models rain motion as an image-space flow, projects the estimated field onto the divergence-free subspace, and converts its local direction and extent into an anisotropic attention bias. The same rain-aware operator is then reused across neighbouring frames and three resolutions in a Loop-in-Loop design. The resulting three-frame model has only 0.80M parameters and remains competitive with substantially larger restoration networks on four benchmarks. Controlled view experiments show that the shared operator benefits most from complementary neighbouring observations rather than repeated computation. To evaluate whether the model remains reliable when rain motion changes across frames, we introduce RainSyn-Gust, which injects controlled changes in rain-streak direction into existing benchmarks. We also use a physics-inspired reference-free diagnostic to analyze removal on real rainy videos.