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Existing methods attempt to improve models' generalization ability on real-world hazy images by exploring well-designed training schemes (\eg, CycleGAN, prior loss).
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Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25
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Fattal, R.: Single image dehazing. ACM transactions on graphics (TOG) 27
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Tan, R.T.: Visibility in bad weather from a single image. In: 2008 IEEE conference on computer vision and pattern recognition. pp. 1–8. IEEE (2008)
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He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: CVPR. pp. 1956–1963 (2009)
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He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence 33
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Fattal, R.: Dehazing using color-lines. ACM transactions on graphics (TOG) 34
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Choi, L.K., You, J., Bovik, A.C.: Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Transactions on Image Processing 24
2015
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Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE transactions on image processing 24
2015
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Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: CVPR. pp. 1674–1682 (2016). https://doi.org/10.1109/CVPR.2016.185
2016
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Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing 25
2016
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Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.H.: Single image dehazing via multi-scale convolutional neural networks. In: European conference on computer vision. pp. 154–169. Springer (2016)
2016
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Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (Oct 2017)
2017
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Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: All-in-one dehazing network. In: Proceedings of the IEEE international conference on computer vision. pp. 4770–4778 (2017)
2017
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Ancuti, C.O., Ancuti, C., Timofte, R., De Vleeschouwer, C.: O-haze: A dehazing benchmark with real hazy and haze-free outdoor images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2018)
2018
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Ancuti, C., Ancuti, C.O., Timofte, R., De Vleeschouwer, C.: I-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Indoor Images. In: CVPRW. pp. 620–631 (2018). https://doi.org/10.1007/978-3-030-01449-0_52, http://link.springer.com/10.1007/978-3-030-01449-0{_}52
2018
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Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., Wang, Z.: Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing 28
2018
Cited alongside, same era.
Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3194–3203 (2018)
2018
Cited alongside, same era.
Ancuti, C.O., Ancuti, C., Sbert, M., Timofte, R.: Dense-Haze: A Benchmark for Image Dehazing with Dense-Haze and Haze-Free Images. In: ICIP. pp. 1014–1018. IEEE (sep 2019). https://doi.org/10.1109/ICIP.2019.8803046, https://ieeexplore.ieee.org/document/8803046/
2019
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Gandelsman, Y., Shocher, A., Irani, M.: "double-dip": Unsupervised image decomposition via coupled deep-image-priors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
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Shyam, P., Yoon, K.J., Kim, K.S.: Towards domain invariant single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 9657–9665 (2021)
2021
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Wu, H., Qu, Y., Lin, S., Zhou, J., Qiao, R., Zhang, Z., Xie, Y., Ma, L.: Contrastive learning for compact single image dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10551–10560 (2021)
2021
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Chen, L., Chu, X., Zhang, X., Sun, J.: Simple baselines for image restoration. In: European Conference on Computer Vision. pp. 17–33. Springer (2022)
2022
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Guo, C.L., Yan, Q., Anwar, S., Cong, R., Ren, W., Li, C.: Image dehazing transformer with transmission-aware 3d position embedding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5812–5820 (2022)
2022
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Golts, A., Freedman, D., Elad, M.: Unsupervised single image dehazing using dark channel prior loss. IEEE Transactions on Image Processing 29
2019
Cited alongside, same era.
Liu, X., Ma, Y., Shi, Z., Chen, J.: Griddehazenet: Attention-based multi-scale network for image dehazing. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 7314–7323 (2019)
2019
Cited alongside, same era.
Ancuti, C.O., Ancuti, C., Timofte, R.: Nh-haze: An image dehazing benchmark with non-homogeneous hazy and haze-free images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2020)
2020
Cited alongside, same era.
Dong, H., Pan, J., Xiang, L., Hu, Z., Zhang, X., Wang, F., Yang, M.H.: Multi-scale boosted dehazing network with dense feature fusion. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2157–2167 (2020)
2020
Cited alongside, same era.
Dong, J., Pan, J.: Physics-based feature dehazing networks. In: European Conference on Computer Vision. pp. 188–204. Springer (2020)
2020
Cited alongside, same era.
Kanti Dhara, S., Roy, M., Sen, D., Kumar Biswas, P.: Color cast dependent image dehazing via adaptive airlight refinement and non-linear color balancing. IEEE Transactions on Circuits and Systems for Video Technology 31
2020
Cited alongside, same era.
Qin, X., Wang, Z., Bai, Y., Xie, X., Jia, H.: Ffa-net: Feature fusion attention network for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 11908–11915 (2020)
2020
Cited alongside, same era.
Shao, Y., Li, L., Ren, W., Gao, C., Sang, N.: Domain adaptation for image dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
2020
Cited alongside, same era.
Hong, M., Liu, J., Li, C., Qu, Y.: Uncertainty-driven dehazing network. Proceedings of the AAAI Conference on Artificial Intelligence 36
2022
Later among the works it cites.
Li, Y., Chang, Y., Gao, Y., Yu, C., Yan, L.: Physically disentangled intra- and inter-domain adaptation for varicolored haze removal. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5841–5850 (June 2022)
2022
Later among the works it cites.
Liu, H., Wu, Z., Li, L., Salehkalaibar, S., Chen, J., Wang, K.: Towards Multi-domain Single Image Dehazing via Test-time Training. In: CVPR. pp. 5831–5840 (2022)
2022
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Yang, Y., Wang, C., Liu, R., Zhang, L., Guo, X., Tao, D.: Self-augmented unpaired image dehazing via density and depth decomposition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2037–2046 (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Yu, H., Huang, J., Liu, Y., Zhu, Q., Zhou, M., Zhao, F.: Source-free domain adaptation for real-world image dehazing. In: Proceedings of the 30th ACM International Conference on Multimedia. pp. 6645–6654 (2022)
2022
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2023
Closest in time.
2023
Closest in time.
Song, Y., He, Z., Qian, H., Du, X.: Vision transformers for single image dehazing. IEEE Transactions on Image Processing 32
2023
Closest in time.
Wu, R.Q., Duan, Z.P., Guo, C.L., Chai, Z., Li, C.: Ridcp: Revitalizing real image dehazing via high-quality codebook priors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22282–22291 (2023)
2023
Closest in time.
2023
Closest in time.
Zheng, Y., Zhan, J., He, S., Dong, J., Du, Y.: Curricular contrastive regularization for physics-aware single image dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5785–5794 (June 2023)
2023
Closest in time.
Gou, Y., Zhao, H., Li, B., Xiao, X., Peng, X.: Test-time degradation adaptation for open-set image restoration. In: Forty-first International Conference on Machine Learning (Jul 2024)
2024
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