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• W4386826603 startingPage "117057" @default.
• W4386826603 abstract "The heavy rain streaks can seriously fade the colour of images and destroy the background texture information. Although the existing deraining methods perform well in rain removal, we find that most of them tend to favor rain removal or restoration. In this paper, we propose a background-detail restoration image deraining network based on convolutional dictionary network to achieve effective deraining results with better-restored background details. Our framework includes two sub-networks, namely the rain-removal network and the background-restoration network. The former sub-network is constructed as the multi-stage architecture. In order to accurately maintain as much background detail as possible while removing rain streaks, we break down the overall rain removal process into more manageable steps. We introduce convolutional dictionary network and Multi-Scale Detail Restoration Block (MSDRB) to achieve the balance between the effectiveness of deraining and background restoration. The second sub-network is Residual-Unet with Squeeze-and-Excitation (RUSE) operation, which can utilize the output of every stage generated from the former sub-network to fine-tune the background details in rain-free images. Experiments on benchmark public synthetic datasets and real-world datasets demonstrate the effectiveness and generalization capability of the proposed method in rain streak removal and background restoration with different rain patterns." @default.
• W4386826603 created "2023-09-19" @default.
• W4386826603 creator A5010224532 @default.
• W4386826603 creator A5043596876 @default.
• W4386826603 date "2023-11-01" @default.
• W4386826603 modified "2023-10-01" @default.
• W4386826603 title "Background-detail restoration image deraining network based on convolutional dictionary network" @default.
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• W4386826603 doi "https://doi.org/10.1016/j.image.2023.117057" @default.
• W4386826603 hasPublicationYear "2023" @default.
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