Unsupervised Deraining : Where Asymmetric Contrastive Learning Meets Self-Similarity
Most existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rain makes them less generalized to complex real rainy scenes. Moreover, the existing methods mainly utilize the property of the image or rain layers...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 46(2024), 5 vom: 01. Apr., Seite 2638-2657
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| 1. Verfasser: |
Chang, Yi
(VerfasserIn) |
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| Weitere Verfasser: |
Guo, Yun,
Ye, Yuntong,
Yu, Changfeng,
Zhu, Lin,
Zhao, Xile,
Yan, Luxin,
Tian, Yonghong |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article |
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