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Real-SR endeavors to produce high-resolution images with rich details while mitigating the impact of multiple degradation factors.
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X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. Change Loy, “Esrgan: Enhanced super-resolution generative adversarial networks,” in Proceedings of the European conference on computer vision (ECCV) workshops , 2018, pp. 0–0
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Y. Zhou, W. Deng, T. Tong, and Q. Gao, “Guided frequency separation network for real-world super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 428–429
2020
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2020
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J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, and R. Timofte, “Swinir: Image restoration using swin transformer,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 1833–1844
2021
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W. Xie, D. Song, C. Xu, C. Xu, H. Zhang, and Y. Wang, “Learning frequency-aware dynamic network for efficient super-resolution,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 4308–4317
2021
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K. Zhang, J. Liang, L. Van Gool, and R. Timofte, “Designing a practical degradation model for deep blind image super-resolution,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 4791–4800
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J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, and R. Timofte, “Swinir: Image restoration using swin transformer,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 1833–1844
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L. Peng, A. Jiang, H. Wei, B. Liu, and M. Wang, “Ensemble single image deraining network via progressive structural boosting constraints,” Signal Processing: Image Communication , vol. 99, p. 116460, 2021
2021
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Q. Yi, J. Li, Q. Dai, F. Fang, G. Zhang, and T. Zeng, “Structure-preserving deraining with residue channel prior guidance,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 4238–4247
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2023
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B. Sun, Y. Zhang, S. Jiang, and Y. Fu, “Hybrid pixel-unshuffled network for lightweight image super-resolution,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 2375–2383
2023
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J. Liu, C. Chen, J. Tang, and G. Wu, “From coarse to fine: Hierarchical pixel integration for lightweight image super-resolution,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 1666–1674
2023
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P. Wei, Z. Xie, G. Li, and L. Lin, “Taylor neural network for real-world image super-resolution,” IEEE Transactions on Image Processing , vol. 32, pp. 1942–1951, 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
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X. Chen, J. Zhang, C. Xu, Y. Wang, C. Wang, and Y. Liu, “Better” cmos” produces clearer images: Learning space-variant blur estimation for blind image super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1651–1661
2023
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B. Xia, Y. Tian, Y. Zhang, Y. Hang, W. Yang, and Q. Liao, “Meta-learning based degradation representation for blind super-resolution,” IEEE Transactions on Image Processing , 2023
2023
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X. Jiang, N. Wang, J. Xin, K. Li, X. Yang, J. Li, X. Wang, and X. Gao, “Fabnet: Frequency-aware binarized network for single image super-resolution,” IEEE Transactions on Image Processing , vol. 32, pp. 6234–6247, 2023
2023
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Z. Yin, M. Liu, X. Li, H. Yang, L. Xiao, and W. Zuo, “Metaf2n: Blind image super-resolution by learning efficient model adaptation from faces,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 13 033–13 044
2023
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Y. Wang*, L. Peng*, L. Li, Y. Cao, and Z.-J. Zha (*Co-first author), “Decoupling-and-aggregating for image exposure correction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 18 115–18 124
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2023
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2023
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2024
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