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Outdoor videos sometimes contain unexpected rain streaks due to the rainyweather, which bring negative effects on subsequent computer visionapplications, e.g., video surveillance, object recognition and tracking, etc.In this paper, we propose a directional regularized tensor-based videoderaining model by taking into consideration the arbitrary direction of rainstreaks. In particular, the sparsity of rain streaks in spatial and derivativedomains, the spatiotemporal sparsity and low-rank property of video backgroundare incorporated into the proposed method. Different from many previous methodsunder the assumption of vertically falling rain streaks, we consider a morerealistic assumption that all the rain streaks in a video fall in anapproximately similar arbitrary direction. The resulting complicatedoptimization problem will be effectively solved through an alternatingdirection method. Comprehensive experiments on both synthetic and realisticdatasets have demonstrated the superiority of the proposed deraining method.

Directional Regularized Tensor Modeling for Video Rain Streaks Removal