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论文类型：期刊论文

发表时间：2019-10-01

发表刊物：SIGNAL PROCESSING-IMAGE COMMUNICATION

收录刊物：EI、SCIE

卷号：78

页面范围：206-215

ISSN号：0923-5965

关键字：Deraining; Convolutional neural network; Fusion connections; Multi-level; Guided learning

摘要：Rainy images severely degrade visibility and make many computer vision algorithms invalid. Hence, it is necessary to remove rain streaks from a single image. In this paper, we propose a novel end-to-end deep learning based deraining method. Previous methods neglect the correlation between different layers with different receptive fields that loss a lot of important information. To better solve the problem, we develop a multi-level guided residual block that is the basic unit of our network. In this block, we utilize multi-level dilation convolutions to obtain different receptive fields and the layer with smaller receptive fields to guide the learning of larger receptive fields. Moreover, in order to reduce the model sizes, the parameters are shared among all multi-level guided residual blocks. Experiments illustrate that guided learning improves the deraining performance and the shared parameters strategy is also feasible. Quantitative and qualitative experimental results demonstrate the superiority of the proposed method compared with several state-of-the-art deraining methods.