UCL-Dehaze : Toward Real-World Image Dehazing via Unsupervised Contrastive Learning
While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain shift problem. From a different yet new perspective, this paper explores contrastive learning with an adversarial...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 33(2024) vom: 21., Seite 1361-1374
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| 1. Verfasser: |
Wang, Yongzhen
(VerfasserIn) |
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| Weitere Verfasser: |
Yan, Xuefeng,
Wang, Fu Lee,
Xie, Haoran,
Yang, Wenhan,
Zhang, Xiao-Ping,
Qin, Jing,
Wei, Mingqiang |
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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 image processing : a publication of the IEEE Signal Processing Society
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| Schlagworte: | Journal Article |
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