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Currently, restoring clean images from a variety of degradation types using a single model is still a challenging task.
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Y. Zhou, D. Ren, N. Emerton, S. Lim, and T. Large, “Image restoration for under-display camera,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 9179–9188
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2019
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2019
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Y. Chen, X. Dai, M. Liu, D. Chen, L. Yuan, and Z. Liu, “Dynamic convolution: Attention over convolution kernels,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2019, pp. 11 027–11 036
2019
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H. Su, V. Jampani, D. Sun, O. Gallo, E. G. Learned-Miller, and J. Kautz, “Pixel-adaptive convolutional neural networks,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2019, pp. 11 158–11 167
2019
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M. Asim, F. Shamshad, and A. Ahmed, “Blind image deconvolution using deep generative priors,” IEEE Trans. Image Process. , vol. 6, pp. 1493–1506, 2020
2020
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2020
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B. Li, X. Liu, P. Hu, Z. Wu, J. Lv, and X. Peng, “All-in-one image restoration for unknown corruption,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 17 431–17 441
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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 Proc. Eur. Conf. Comput. Vis. (ECCV) , 2022, pp. 447–464
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2023
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Z. Fu, Y. Yang, X. Tu, Y. Huang, X. Ding, and K.-K. Ma, “Learning a simple low-light image enhancer from paired low-light instances,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 22 252–22 261
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X. Xu, R. Wang, and J. Lu, “Low-light image enhancement via structure modeling and guidance,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 9893–9903
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 Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 5825–5835
2023
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V. Potlapalli, S. W. Zamir, S. Khan, and F. S. Khan, “Promptir: prompting for all-in-one blind image restoration,” in Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , 2023
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J. Song, B. Chen, and J. Zhang, “Dynamic path-controllable deep unfolding network for compressive sensing,” IEEE Trans. Image Process. , vol. 32, pp. 2202–2214, 2023
2023
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M. V. Conde, G. Geigle, and R. Timofte, “Instructir: High-quality image restoration following human instructions,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , 2024, pp. 1–21
2024
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W. Li, G. Chen, and Y. Chang, “An efficient single image de-raining model with decoupled deep networks,” IEEE Trans. Image Process. , vol. 33, pp. 69–81, 2024
2024
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Z. Wang, J. Liu, G. Li, and H. Han, “Blind2unblind: Self-supervised image denoising with visible blind spots,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 2027–2036
2036
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