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Indexed by:Journal Papers

Date of Publication:2019-10-01

Journal:SIGNAL PROCESSING-IMAGE COMMUNICATION

Included Journals:EI、SCIE

Volume:78

Page Number:206-215

ISSN No.:0923-5965

Key Words:Deraining; Convolutional neural network; Fusion connections; Multi-level; Guided learning

Abstract: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.