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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3064229
|2 doi
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|a eng
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| 100 |
1 |
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|a Wang, Guoqing
|e verfasserin
|4 aut
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| 245 |
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|a Attentive Feature Refinement Network for Single Rainy Image Restoration
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|c 2021
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 24.03.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Despite the fact that great progress has been made on single image deraining tasks, it is still challenging for existing models to produce satisfactory results directly, and it often requires a single or multiple refinement stages to gradually improve the quality. However, in this paper, we demonstrate that existing image-level refinement with a stage-independent learning design is problematic with the side effect of over/under-deraining. To resolve this issue, we for the first time propose the mechanism of learning to carry out refinement on the unsatisfactory features, and propose a novel attentive feature refinement (AFR) module. Specifically, AFR is designed as a two-branched network for simultaneous rain-distribution-aware attention map learning and attention guided hierarchy-preserving feature refinement. Guided by task-specific attention, coarse features are progressively refined to better model the diversified rainy effects. By using a separable convolution as the basic component, our AFR module introduces little computation overhead and can be readily integrated into most rainy-to-clean image translation networks for achieving better deraining results. By incorporating a series of AFR modules into a general encoder-decoder network, AFR-Net is constructed for deraining and it achieves new state-of-the-art results on both synthetic and real images. Furthermore, by using AFR-Net as a teacher model, we explore the use of knowledge distillation to successfully learn a student model that is also able to achieve state-of-the-art results but with a much faster inference speed (i.e., it only takes 0.08 second to process a 512×512 rainy image). Code and pre-trained models are available at 〈 https://github.com/RobinCSIRO/AFR-Net 〉
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|a Journal Article
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|a Sun, Changming
|e verfasserin
|4 aut
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| 700 |
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|a Sowmya, Arcot
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 17., Seite 3734-3747
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|x 1941-0042
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|g volume:30
|g year:2021
|g day:17
|g pages:3734-3747
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|u http://dx.doi.org/10.1109/TIP.2021.3064229
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