Distribution-decouple learning network: an innovative approach for single image dehazing with spatial and frequency decoupling

Author:

Wu Yabo,Li Wenting,Chen Ziyang,Wen Hui,Cui Zhongwei,Zhang Yongjun

Publisher

Springer Science and Business Media LLC

Reference56 articles.

1. Guo, X., Yang, Y., Wang, C., Ma, J.: Image dehazing via enhancement, restoration, and fusion: a survey. Inf. Fusion 86, 146–170 (2022)

2. Li, Y., Cheng, D., Zhang, D., Wang, N., Gao, X., Sun, J.: Single image dehazing with an independent detail-recovery network. Knowl. Based Syst. 254, 109579 (2022)

3. Gui, J., Cong, X., Cao, Y., Ren, W., Zhang, J., Zhang, J., Cao, J., Tao, D.: A comprehensive survey and taxonomy on single image dehazing based on deep learning. ACM Comput. Surv. 55(13s), 1–37 (2023)

4. Wang, S., Mei, X., Kang, P., Li, Y., Liu, D.: Dfc-dehaze: an improved cycle-consistent generative adversarial network for unpaired image dehazing. Vis. Comput. 40, 1–12 (2023)

5. Zhang, Z., Zhao, L., Liu, Y., Zhang, S., Yang, J.: Unified density-aware image dehazing and object detection in real-world hazy scenes. In: Proceedings of the Asian Conference on Computer Vision (2020)

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