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Recent efforts on image restoration have focused on developing "all-in-one" models that can handle different degradation types and levels within single model.
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2020
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2022
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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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2021
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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
Cited alongside, same era.
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2021
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Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022
2021
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2021
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H. Chen, Y. Wang, T. Guo, C. Xu, Y. Deng, Z. Liu, S. Ma, C. Xu, C. Xu, and W. Gao, “Pre-trained image processing transformer,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 12 299–12 310
2021
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2021
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X. Chen, H. Li, M. Li, and J. Pan, “Learning a sparse transformer network for effective image deraining,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5896–5905
2023
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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
2023
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2023
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V. Potlapalli, S. W. Zamir, S. H. Khan, and F. Shahbaz Khan, “Promptir: Prompting for all-in-one image restoration,” Advances in Neural Information Processing Systems , vol. 37, 2023
2023
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2023
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J. Wang, W. Zhu, P. Wang, X. Yu, L. Liu, M. Omar, and R. Hamid, “Selective structured state-spaces for long-form video understanding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 6387–6397
2023
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M. M. Islam, M. Hasan, K. S. Athrey, T. Braskich, and G. Bertasius, “Efficient movie scene detection using state-space transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 18 749–18 758
2023
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S. Wang, C. Saharia, C. Montgomery, J. Pont-Tuset, S. Noy, S. Pellegrini, Y. Onoe, S. Laszlo, D. J. Fleet, R. Soricut et al. , “Imagen editor and editbench: Advancing and evaluating text-guided image inpainting,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 18 359–18 369
2023
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O. Torun, S. E. Yuksel, E. Erdem, N. Imamoglu, and A. Erdem, “Hyperspectral image denoising via self-modulating convolutional neural networks,” Signal Processing , vol. 214, p. 109248, 2024
2024
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B. Xiao, Z. Zheng, Y. Zhuang, C. Lyu, and X. Jia, “Single uhd image dehazing via interpretable pyramid network,” Signal Processing , vol. 214, p. 109225, 2024
2024
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A. Kumari and S. K. Sahoo, “A new fast and efficient dehazing and defogging algorithm for single remote sensing images,” Signal Processing , vol. 215, p. 109289, 2024
2024
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Y. Liu, Y. Tian, Y. Zhao, H. Yu, L. Xie, Y. Wang, Q. Ye, and Y. Liu, “Vmamba: Visual state space model,” Advances in Neural Information Processing Systems , 2024
2024
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2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Z. Luo, F. K. Gustafsson, Z. Zhao, J. Sjölund, and T. B. Schön, “Controlling vision-language models for multi-task image restoration,” in The Twelfth International Conference on Learning Representations , Vienna Austria, 2024
2024
Closest in time.
Z. Wang, J. Liu, G. Li, and H. Han, “Blind2unblind: Self-supervised image denoising with visible blind spots,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 2027–2036
2036
Closest in time.