Spatial-Temporal Integration Network with Self-Guidance for Robust Video Deraining

Recently, video deraining has become a research focus. Network-based approaches are continuously showing extrusive performance. However, they lack precise control over the motion consistency in temporal information and characterize spatial distribution, so that their results are unsatisfying, especially in some real-world scenarios. To settle them, we develop a spatial-temporal integration network with self-guidance. It contains flow-induced alignment, self-guidance generation, and spatial-temporal integration modules. The alignment module not only preliminarily removes rain to provide more effective temporal correlation but also accurately keeps motion consistency between frames. The self-guidance map characterizes the pixel-level spatial distribution for the target to avoid injuring the background. Finally, we concatenate adjacent aligned frames, self-guidance map, and original current rain frame into the integration module to progressively fuse them in a coarse-to-fine way. Extensive evaluations demonstrate our superiority against other state-of-the-art methods qualitatively and quantitatively.