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Remote sensing image dehazing (RSID) aims to remove nonuniform and physically irregular haze factors for high-quality image restoration.
K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE TPAMI , vol. 33, no. 12, pp. 2341–2353, 2010
2010
Earlier work this paper cites.
J. Long, Z. Shi, W. Tang, and C. Zhang, “Single remote sensing image dehazing,” IEEE GRSL , vol. 11, no. 1, pp. 59–63, 2013
2013
Earlier work this paper cites.
B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng, “Aod-net: All-in-one dehazing network,” in ICCV , 2017, pp. 4770–4778
2017
Earlier work this paper cites.
D. Chen, M. He, Q. Fan, J. Liao, L. Zhang, D. Hou, L. Yuan, and G. Hua, “Gated context aggregation network for image dehazing and deraining,” in WACV , 2019, pp. 1375–1383
2019
Earlier work this paper cites.
X. Liu, Y. Ma, Z. Shi, and J. Chen, “Griddehazenet: Attention-based multi-scale network for image dehazing,” in ICCV , 2019, pp. 7314–7323
2019
Earlier work this paper cites.
X. Cong, J. Gui, K.-C. Miao, J. Zhang, B. Wang, and P. Chen, “Discrete haze level dehazing network,” in ACMMM , 2020, pp. 1828–1836
2020
Earlier work this paper cites.
X. Qin, Z. Wang, Y. Bai, X. Xie, and H. Jia, “Ffa-net: Feature fusion attention network for single image dehazing,” in AAAI , vol. 34, no. 07, 2020, pp. 11 908–11 915
2020
Earlier work this paper cites.
H. Dong, J. Pan, L. Xiang, Z. Hu, X. Zhang, F. Wang, and M.-H. Yang, “Multi-scale boosted dehazing network with dense feature fusion,” in CVPR , 2020, pp. 2157–2167
2020
Earlier work this paper cites.
Y. Li and X. Chen, “A coarse-to-fine two-stage attentive network for haze removal of remote sensing images,” IEEE GRSL , vol. 18, no. 10, pp. 1751–1755, 2020
2020
Cited alongside, same era.
J. Gui, X. Cong, Y. Cao, W. Ren, J. Zhang, J. Zhang, and D. Tao, “A comprehensive survey on image dehazing based on deep learning,” in IJCAI , 2021
2021
Cited alongside, same era.
X. Chen, Y. Li, L. Dai, and C. Kong, “Hybrid high-resolution learning for single remote sensing satellite image dehazing,” IEEE GRSL , vol. 19, pp. 1–5, 2021
2021
Cited alongside, same era.
H. Ullah, K. Muhammad, M. Irfan, S. Anwar, M. Sajjad, A. S. Imran, and V. H. C. de Albuquerque, “Light-dehazenet: a novel lightweight cnn architecture for single image dehazing,” IEEE TIP , vol. 30, pp. 8968–8982, 2021
2021
Cited alongside, same era.
Y. Song, Z. He, H. Qian, and X. Du, “Vision transformers for single image dehazing,” IEEE TIP , vol. 32, pp. 1927–1941, 2023
2023
Later among the works it cites.
A. Kulkarni and S. Murala, “Aerial image dehazing with attentive deformable transformers,” in WACV , 2023, pp. 6305–6314
2023
Later among the works it cites.
T. Song, S. Fan, P. Li, J. Jin, G. Jin, and L. Fan, “Learning an effective transformer for remote sensing satellite image dehazing,” IEEE GRSL , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Chen, H. Li, M. Li, and J. Pan, “Learning a sparse transformer network for effective image deraining,” in CVPR , 2023, pp. 5896–5905
2023
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2022
Cited alongside, same era.
C.-L. Guo, Q. Yan, S. Anwar, R. Cong, W. Ren, and C. Li, “Image dehazing transformer with transmission-aware 3d position embedding,” in CVPR , 2022, pp. 5812–5820
2022
Cited alongside, same era.
S. Li, Y. Zhou, and W. Xiang, “M2scn: Multi-model self-correcting network for satellite remote sensing single-image dehazing,” IEEE GRSL , vol. 20, pp. 1–5, 2022
2022
Cited alongside, same era.
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang, “Restormer: Efficient transformer for high-resolution image restoration,” in CVPR , 2022, pp. 5728–5739
2022
Cited alongside, same era.
Later among the works it cites.
X. He, T. Jia, and J. Li, “Learning degradation-aware visual prompt for maritime image restoration under adverse weather conditions,” Frontiers in Marine Science , vol. 11, p. 1382147, 2024
2024
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2024
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