Cycle contrastive adversarial learning with structural consistency for unsupervised high-quality image deraining transformer

Author:

Zhao Chen,Cai WeilingORCID,Hu Chengwei,Yuan Zheng

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference98 articles.

1. Aakerberg, A., Johansen, A. S., Nasrollahi, K., & Moeslund, T. B. (2022). Semantic segmentation guided real-world super-resolution. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 449–458).

2. Unsupervised deraining: Where asymmetric contrastive learning meets self-similarity;Chang,2022

3. Chang, Y., Yan, L., & Zhong, S. (2017). Transformed low-rank model for line pattern noise removal. In Proceedings of the IEEE international conference on computer vision (pp. 1726–1734).

4. Improved baselines with momentum contrastive learning;Chen,2020

5. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In International conference on machine learning (pp. 1597–1607).

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1. ECT: Efficient Cross Transformer for Image Deblurring;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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