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Due to the lack of natural scene and haze prior information, it is greatly challenging to completely remove the haze from a single image without distorting its visual content.
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Y. Dong, Y. Liu, H. Zhang, S. Chen, and Y. Qiao, “Fd-gan: Generative adversarial networks with fusion-discriminator for single image dehazing,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 10 729–10 736
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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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2157–2167
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M. Yu, V. Cherukuri, T. Guo, and V. Monga, “Ensemble dehazing networks for non-homogeneous haze,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 450–451
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H. Li, Q. Wu, K. N. Ngan, H. Li, and F. Meng, “Region adaptive two-shot network for single image dehazing,” in 2020 IEEE International Conference on Multimedia and Expo (ICME) . IEEE, 2020, pp. 1–6
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C. O. Ancuti, C. Ancuti, and R. Timofte, “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 Workshops , 2020, pp. 444–445
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M. Ju, C. Ding, W. Ren, Y. Yang, D. Zhang, and Y. J. Guo, “Ide: Image dehazing and exposure using an enhanced atmospheric scattering model,” IEEE Transactions on Image Processing , vol. 30, pp. 2180–2192, 2021
2021
Closest in time.
B. Ganguly, A. Bhattacharya, A. Srivastava, D. Dey, and S. Munshi, “Single image haze removal with haze map optimization for various haze concentrations,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
2021
Closest in time.
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2021
Closest in time.