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Rain removal is important for improving the robustness of outdoor visionbased systems. Current rain removal methods show limitations either for complexdynamic scenes shot from fast moving cameras, or under torrential rain fallwith opaque occlusions. We propose a novel derain algorithm, which appliessuperpixel (SP) segmentation to decompose the scene into depth consistentunits. Alignment of scene contents are done at the SP level, which proves to berobust towards rain occlusion and fast camera motion. Two alignment outputtensors, i.e., optimal temporal match tensor and sorted spatial-temporal matchtensor, provide informative clues for rain streak location and occludedbackground contents to generate an intermediate derain output. These tensorswill be subsequently prepared as input features for a convolutional neuralnetwork to restore high frequency details to the intermediate output forcompensation of mis-alignment blur. Extensive evaluations show that up to 5 dBreconstruction PSNR advantage is achieved over state-of-the-art methods. Visualinspection shows that much cleaner rain removal is achieved especially forhighly dynamic scenes with heavy and opaque rainfall from a fast moving camera.

Robust Video Content Alignment and Compensation for Rain Removal in a CNN Framework