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• W2948667002 abstract "In this paper, we address the problem of rain removal from videos by proposing a more comprehensive framework that considers the additional degradation factors in real scenes neglected in previous works. The proposed framework is built upon a two-stage recurrent network with dual-level flow regularizations to perform the inverse recovery process of the rain synthesis model for video deraining. The rain-free frame is estimated from the single rain frame at the first stage. It is then taken as guidance along with previously recovered clean frames to help obtain a more accurate clean frame at the second stage. This two-step architecture is capable of extracting more reliable motion information from the initially estimated rain-free frame at the first stage for better frame alignment and motion modeling at the second stage. Furthermore, to keep the motion consistency between frames that facilitates a frame-consistent deraining model at the second stage, a dual-level flow based regularization is proposed at both coarse flow and fine pixel levels. To better train and evaluate the proposed video deraining network, a novel rain synthesis model is developed to produce more visually authentic paired training and evaluation videos. Extensive experiments on a series of synthetic and real videos verify not only the superiority of the proposed method over state-of-the-art but also the effectiveness of network design and its each component." @default.
• W2948667002 created "2019-06-14" @default.
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• W2948667002 date "2019-06-01" @default.
• W2948667002 modified "2023-09-30" @default.
• W2948667002 title "Frame-Consistent Recurrent Video Deraining With Dual-Level Flow" @default.
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• W2948667002 doi "https://doi.org/10.1109/cvpr.2019.00176" @default.
• W2948667002 hasPublicationYear "2019" @default.
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