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Rain removal has recently attracted increasing research attention, as it isable to enhance the visibility of rain videos. However, the existing learningbased rain removal approaches for videos suffer from insufficient trainingdata, especially when applying deep learning to remove rain. In this paper, weestablish a large-scale video database for rain removal (LasVR), which consistsof 316 rain videos. Then, we observe from our database that there exist thetemporal correlation of clean content and similar patterns of rain across videoframes. According to these two observations, we propose a two-streamconvolutional long- and short- term memory (ConvLSTM) approach for rain removalin videos. The first stream is composed of the subnet for rain detection, whilethe second stream is the subnet of rain removal that leverages the featuresfrom the rain detection subnet. Finally, the experimental results on bothsynthetic and real rain videos show the proposed approach performs better thanother state-of-the-art approaches.

Removing Rain in Videos: A Large-scale Database and A Two-stream ConvLSTM Approach