  Outdoor videos sometimes contain unexpected rain streaks due to the rainy
weather, which bring negative effects on subsequent computer vision
applications, e.g., video surveillance, object recognition and tracking, etc.
In this paper, we propose a directional regularized tensor-based video
deraining model by taking into consideration the arbitrary direction of rain
streaks. In particular, the sparsity of rain streaks in spatial and derivative
domains, the spatiotemporal sparsity and low-rank property of video background
are incorporated into the proposed method. Different from many previous methods
under the assumption of vertically falling rain streaks, we consider a more
realistic assumption that all the rain streaks in a video fall in an
approximately similar arbitrary direction. The resulting complicated
optimization problem will be effectively solved through an alternating
direction method. Comprehensive experiments on both synthetic and realistic
datasets have demonstrated the superiority of the proposed deraining method.
