딥러닝 기반 Deraining 기법 비교 및 연구 동향
Deep Learning-based Deraining: Performance Comparison and Trends
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초록
Deraining is one of the image restoration tasks and should consider a tradeoff between local details and broad contextual information while recovering images. Current studies adopt an attention mechanism which has been actively researched in natural language processing to deal with both global and local features. This paper classifies existing deraining methods and provides comparative analysis and performance comparison by using several datasets in terms of generalization.
Abstract
Deraining is one of the image restoration tasks and should consider a tradeoff between local details and broad contextual information while recovering images. Current studies adopt an attention mechanism which has been actively researched in natural language processing to deal with both global and local features. This paper classifies existing deraining methods and provides comparative analysis and performance comparison by using several datasets in terms of generalization.
참고문헌 (13)
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- S. W. Zamir, "Multi-stage Progressive Image Restoration", Proceedings of the IEEE, 2021, ~
- N. Ahn, "EAGNet: Elementwise Attentive Gating Network-Based Single Image De-raining with Rain Simplification", IEEE Transactions on Circuits and Systems for Video Technology (2021)
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- X. Fu, "Removing Rain from Single Images via a Deep Detail Network", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
- S. Li, "Single Image Deraining: A Comprehensive Benchmark Analysis", Proceedings of the IEEE, 2019, ~
- "You Only Learn One Representation: Unified Network for Multiple Tasks" (2021)[URL]