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Existing methods have demonstrated effective performance on a single degradation type.
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2017
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2017
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2017
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2017
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2017
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2018
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2018
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2018
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2018
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2018
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2018
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2018
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2018
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2018
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R. Yasarla and V. M. Patel, “Uncertainty guided multi-scale residual learning-using a cycle spinning cnn for single image de-raining,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 8405–8414
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2019
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2019
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2021
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2021
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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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5728–5739
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Cited alongside, same era.
Y. Qu, Y. Chen, J. Huang, and Y. Xie, “Enhanced pix2pix dehazing network,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 8160–8168
2019
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A. Krull, T.-O. Buchholz, and F. Jug, “Noise2void-learning denoising from single noisy images,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2129–2137
2019
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W. Yang, R. T. Tan, J. Feng, Z. Guo, S. Yan, and J. Liu, “Joint rain detection and removal from a single image with contextualized deep networks,” IEEE transactions on pattern analysis and machine intelligence , vol. 42, no. 6, pp. 1377–1393, 2019
2019
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J. Batson and L. Royer, “Noise2self: Blind denoising by self-supervision,” in International Conference on Machine Learning . PMLR, 2019, pp. 524–533
2019
Cited alongside, same era.
B. Li, W. Ren, D. Fu, D. Tao, D. Feng, W. Zeng, and Z. Wang, “Benchmarking single-image dehazing and beyond,” IEEE Transactions on Image Processing , vol. 28, no. 1, pp. 492–505, 2019
2019
Cited alongside, same era.
J. He, C. Dong, and Y. Qiao, “Modulating image restoration with continual levels via adaptive feature modification layers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 11 056–11 064
2019
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C. Tian, Y. Xu, and W. Zuo, “Image denoising using deep cnn with batch renormalization,” Neural Networks , vol. 121, pp. 461–473, 2020
2020
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K. Jiang, Z. Wang, P. Yi, C. Chen, B. Huang, Y. Luo, J. Ma, and J. Jiang, “Multi-scale progressive fusion network for single image deraining,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 8346–8355
2020
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2022
Later among the works it cites.
B. Li, X. Liu, P. Hu, Z. Wu, J. Lv, and X. Peng, “All-in-one image restoration for unknown corruption,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 452–17 462
2022
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2022
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Y. Zhou, C. Lin, D. Luo, Y. Liu, Y. Tai, C. Wang, and M. Chen, “Joint learning content and degradation aware feature for blind super-resolution,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, pp. 2606–2616
2022
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2022
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2022
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2022
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2022
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L. Liu, L. Xie, X. Zhang, S. Yuan, X. Chen, W. Zhou, H. Li, and Q. Tian, “Tape: Task-agnostic prior embedding for image restoration,” in European Conference on Computer Vision . Springer, 2022, pp. 447–464
2022
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2022
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2022
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Y. Liang, S. Anwar, and Y. Liu, “Drt: A lightweight single image deraining recursive transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 589–598
2022
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L. Chen, X. Chu, X. Zhang, and J. Sun, “Simple baselines for image restoration,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VII . Springer, 2022, pp. 17–33
2022
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R. Xu, M. Yao, and Z. Xiong, “Zero-shot dual-lens super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9130–9139
2023
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M. Yao, D. He, X. Li, F. Li, and Z. Xiong, “Towards interactive self-supervised denoising,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
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Z. Zhang, Y. Wei, H. Zhang, Y. Yang, S. Yan, and M. Wang, “Data-driven single image deraining: A comprehensive review and new perspectives,” Pattern Recognition , p. 109740, 2023
2023
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X. Chen, X. Wang, J. Zhou, Y. Qiao, and C. Dong, “Activating more pixels in image super-resolution transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 22 367–22 377
2023
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C. Zhang, Y. Zhu, Q. Yan, J. Sun, and Y. Zhang, “All-in-one multi-degradation image restoration network via hierarchical degradation representation,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023, pp. 2285–2293
2023
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2023
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D. Park, B. H. Lee, and S. Y. Chun, “All-in-one image restoration for unknown degradations using adaptive discriminative filters for specific degradations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5815–5824
2023
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J. Zhang, J. Huang, M. Yao, Z. Yang, H. Yu, M. Zhou, and F. Zhao, “Ingredient-oriented multi-degradation learning for image restoration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5825–5835
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Y. Song, Z. He, H. Qian, and X. Du, “Vision transformers for single image dehazing,” IEEE Transactions on Image Processing , vol. 32, pp. 1927–1941, 2023
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