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Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature.
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P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” CVPR , 2017
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B. Ummenhofer, H. Zhou, J. Uhrig, N. Mayer, E. Ilg, A. Dosovitskiy, and T. Brox, “Demon: Depth and motion network for learning monocular stereo,” CVPR , 2017
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2015
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S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of The 32nd International Conference on Machine Learning , 2015, pp. 448–456
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X. Shen, C. Zhou, L. Xu, and J. Jia, “Mutual-structure for joint filtering,” in ICCV , 2015, pp. 3406–3414
2015
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Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Transactions on Image Processing , vol. 24, no. 11, pp. 3522–3533, 2015
2015
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W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M.-H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in ECCV . Springer, 2016, pp. 154–169
2016
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B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “Dehazenet: An end-to-end system for single image haze removal,” IEEE TIP , vol. 25, no. 11, pp. 5187–5198, 2016
2016
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D. Berman, S. Avidan et al. , “Non-local image dehazing,” in CVPR , 2016, pp. 1674–1682
2016
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2017
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D. Berman, T. Treibitz, and S. Avidan, “Air-light estimation using haze-lines,” in Computational Photography (ICCP), 2017 IEEE International Conference on . IEEE, 2017, pp. 1–9
2017
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X. Yang, Z. Xu, and J. Luo, “Towards perceptual image dehazing by physics-based disentanglement and adversarial training,” 2018
2018
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H. Zhang and V. M. Patel, “Densely connected pyramid dehazing network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3194–3203
2018
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H. Zhang, V. Sindagi, and V. M. Patel, “Multi-scale single image dehazing using perceptual pyramid deep network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 902–911
2018
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R. Li, J. Pan, Z. Li, and J. Tang, “Single image dehazing via conditional generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8202–8211
2018
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D. Yang and J. Sun, “Proximal dehaze-net: A prior learning-based deep network for single image dehazing,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 702–717
2018
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2018
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2018
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J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang, “Generative image inpainting with contextual attention,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5505–5514
2018
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2018
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2018
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C. Yan, H. Xie, J. Chen, Z. Zha, X. Hao, Y. Zhang, and Q. Dai, “A fast uyghur text detector for complex background images,” IEEE Transactions on Multimedia , vol. 20, no. 12, pp. 3389–3398, 2018
2018
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C. Yan, H. Xie, D. Yang, J. Yin, Y. Zhang, and Q. Dai, “Supervised hash coding with deep neural network for environment perception of intelligent vehicles,” IEEE transactions on intelligent transportation systems , vol. 19, no. 1, pp. 284–295, 2018
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C. Yan, H. Xie, S. Liu, J. Yin, Y. Zhang, and Q. Dai, “Effective uyghur language text detection in complex background images for traffic prompt identification,” IEEE transactions on intelligent transportation systems , vol. 19, no. 1, pp. 220–229, 2018
2018
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W. Ren, J. Zhang, X. Xu, L. Ma, X. Cao, G. Meng, and W. Liu, “Deep video dehazing with semantic segmentation,” IEEE Transactions on Image Processing , vol. 28, no. 4, pp. 1895–1908, 2019
2019
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2019
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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 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) . IEEE, 2019, pp. 1375–1383
2019
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