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Natural images are often degraded by complex, composite degradations such as rain, snow, and haze, which adversely impact downstream vision applications.
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J. M. J. Valanarasu, R. Yasarla, and V. M. Patel, “Transweather: Transformer-based restoration of images degraded by adverse weather conditions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 2353–2363
2022
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Y. Wang, C. Ma, and J. Liu, “Smartassign: Learning A smart knowledge assignment strategy for deraining and desnowing,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 3677–3686
2023
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Y. Quan, X. Tan, Y. Huang, Y. Xu, and H. Ji, “Image desnowing via deep invertible separation,” IEEE Trans. Circuits Syst. Video Technol. , vol. 33, no. 7, pp. 3133–3144, 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 Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 5815–5824
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V. Potlapalli, S. W. Zamir, S. H. Khan, and F. S. Khan, “Promptir: Prompting for all-in-one image restoration,” in Proc. Annu. Conf. Neural Inf. Process. Syst. (NIPS)) , 2023
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Y. Li, Y. Fan, X. Xiang, D. Demandolx, R. Ranjan, R. Timofte, and L. V. Gool, “Efficient and explicit modelling of image hierarchies for image restoration,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2023, pp. 18 278–18 289
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2024
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S. Zhang, Q. Dong, W. Mao, and Z. Wang, “A unified accelerator for all-in-one image restoration based on prompt degradation learning,” IEEE Trans. Circuits Syst. I Regul. Pap. , vol. 72, no. 3, pp. 1282–1295, 2025
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Y. Cui, S. W. Zamir, S. Khan, A. Knoll, M. Shah, and F. S. Khan, “AdaIR: Adaptive all-in-one image restoration via frequency mining and modulation,” in Proc. Int. Conf. Learn. Represent. (ICLR) , 2025
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