Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning-Reference-Cited by-同舟云学术

Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning

Published:2022 Issue: Volume: Page:632-648
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Chen Xiang,Fan Zhentao,Li Pengpeng,Dai Longgang,Kong Caihua,Zheng Zhuoran,Huang Yufeng,Li Yufeng

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-19790-1_38

Reference56 articles.

1. Anvari, Z., Athitsos, V.: Dehaze-GLCGAN: unpaired single image de-hazing via adversarial training. arXiv preprint arXiv:2008.06632 (2020)

2. Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: DehazeNet: an end-to-end system for single image haze removal. IEEE TIP 25(11), 5187–5198 (2016)

3. Chang, C.M., Sung, C.S., Lin, T.N.: DAMix: density-aware data augmentation for unsupervised domain adaptation on single image dehazing. arXiv preprint arXiv:2109.12544 (2021)

4. Chen, D., et al.: Gated context aggregation network for image dehazing and deraining. In: WACV, pp. 1375–1383. IEEE (2019)

5. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML, pp. 1597–1607. PMLR (2020)

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