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• W3034972231 abstract "This paper looks at this intriguing question: are single images with their details lost during deraining, reversible to their artifact-free status? We propose an end-to-end detail-recovery image deraining network (termed a DRDNet) to solve the problem. Unlike existing image deraining approaches that attempt to meet the conflicting goal of simultaneously deraining and preserving details in a unified framework, we propose to view rain removal and detail recovery as two seperate tasks, so that each part could specialize rather than trade-off between two conflicting goals. Specifically, we introduce two parallel sub-networks with a comprehensive loss function which synergize to derain and recover the lost details caused by deraining. For complete rain removal, we present a rain residual network with the squeeze-and-excitation (SE) operation to remove rain streaks from the rainy images. For detail recovery, we construct a specialized detail repair network consisting of welldesigned blocks, named structure detail context aggregation block (SDCAB), to encourage the lost details to return for eliminating image degradations. Moreover, the detail recovery branch of our proposed detail repair framework is detachable and can be incorporated into existing deraining methods to boost their performances. DRD-Net has been validated on several well-known benchmark datasets in terms of deraining robustness and detail accuracy. Comparisons show clear visual and numerical improvements of our method over the state-of-the-arts." @default.
• W3034972231 created "2020-06-19" @default.
• W3034972231 creator A5036726873 @default.
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• W3034972231 date "2020-06-01" @default.
• W3034972231 modified "2023-10-11" @default.
• W3034972231 title "Detail-recovery Image Deraining via Context Aggregation Networks" @default.
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• W3034972231 doi "https://doi.org/10.1109/cvpr42600.2020.01457" @default.
• W3034972231 hasPublicationYear "2020" @default.
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