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This work aims to tackle the all-in-one image restoration task, which seeks to handle multiple types of degradation with a single model.
X. Mao, Y. Liu, F. Liu, Q. Li, W. Shen, and Y. Wang, “Intriguing findings of frequency selection for image deblurring,” in AAAI , vol. 37, no. 2, 2023, pp. 1905–1913
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K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 33, no. 12, pp. 2341–2353, 2011
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J.-B. Huang, A. Singh, and N. Ahuja, “Single image super-resolution from transformed self-exemplars,” in CVPR , 2015
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W. Ren, X. Cao, J. Pan, X. Guo, W. Zuo, and M.-H. Yang, “Image deblurring via enhanced low-rank prior,” IEEE Transactions on Image Processing , vol. 25, no. 7, pp. 3426–3437, 2016
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B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “Dehazenet: An end-to-end system for single image haze removal,” IEEE Transactions on Image Processing , vol. 25, no. 11, pp. 5187–5198, 2016
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J. Pan, D. Sun, H. Pfister, and M.-H. Yang, “Blind image deblurring using dark channel prior,” in CVPR , 2016
2016
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K. Ma, Z. Duanmu, Q. Wu, Z. Wang, H. Yong, H. Li, and L. Zhang, “Waterloo exploration database: New challenges for image quality assessment models,” IEEE Transactions on Image Processing , vol. 26, no. 2, pp. 1004–1016, 2016
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K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,” IEEE Transactions on Image Processing , vol. 26, no. 7, pp. 3142–3155, 2017
2017
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X. Fu, J. Huang, D. Zeng, Y. Huang, X. Ding, and J. Paisley, “Removing rain from single images via a deep detail network,” in CVPR , 2017
2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in NeurIPS , 2017
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Y. Tai, J. Yang, X. Liu, and C. Xu, “Memnet: A persistent memory network for image restoration,” in ICCV , 2017
2017
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K. Zhang, W. Zuo, S. Gu, and L. Zhang, “Learning deep cnn denoiser prior for image restoration,” in CVPR , 2017
2017
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S. Nah, T. Hyun Kim, and K. Mu Lee, “Deep multi-scale convolutional neural network for dynamic scene deblurring,” in CVPR , 2017
2017
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K. Zhang, W. Zuo, and L. Zhang, “Ffdnet: Toward a fast and flexible solution for CNN based image denoising,” IEEE Transactions on Image Processing , 2018
2018
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H. Zhang and V. M. Patel, “Density-aware single image de-raining using a multi-stream dense network,” in CVPR , 2018
2018
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O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: Blind motion deblurring using conditional adversarial networks,” in CVPR , 2018
2018
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D. Liu, B. Wen, Y. Fan, C. C. Loy, and T. S. Huang, “Non-local recurrent network for image restoration,” in NeurIPS , 2018
2018
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N. Kwak, J. Yoo, and S.-h. Lee, “Image restoration by estimating frequency distribution of local patches,” in CVPR , 2018
2018
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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, 2018
2018
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C. Wei, W. Wang, W. Yang, and J. Liu, “Deep retinex decomposition for low-light enhancement,” in BMVC , 2018
2018
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A. Abdelhamed, S. Lin, and M. S. Brown, “A high-quality denoising dataset for smartphone cameras,” in CVPR , 2018
2018
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W. Wei, D. Meng, Q. Zhao, Z. Xu, and Y. Wu, “Semi-supervised transfer learning for image rain removal,” in CVPR , 2019
2019
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D. Ren, W. Zuo, Q. Hu, P. Zhu, and D. Meng, “Progressive image deraining networks: A better and simpler baseline,” in CVPR , 2019
2019
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H.-H. Yang and Y. Fu, “Wavelet u-net and the chromatic adaptation transform for single image dehazing,” in ICIP , 2019
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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X. Fu, B. Liang, Y. Huang, X. Ding, and J. Paisley, “Lightweight pyramid networks for image deraining,” IEEE transactions on neural networks and learning systems , vol. 31, no. 6, pp. 1794–1807, 2019
2019
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H. Gao, X. Tao, X. Shen, and J. Jia, “Dynamic scene deblurring with parameter selective sharing and nested skip connections,” in CVPR , 2019
2019
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W. Yang, R. T. Tan, S. Wang, Y. Fang, and J. Liu, “Single image deraining: From model-based to data-driven and beyond,” IEEE Transactions on pattern analysis and machine intelligence , vol. 43, no. 11, pp. 4059–4077, 2020
2020
Cited alongside, same era.
Y. Zheng, X. Yu, M. Liu, and S. Zhang, “Single-image deraining via recurrent residual multiscale networks,” IEEE transactions on neural networks and learning systems , vol. 33, no. 3, pp. 1310–1323, 2020
2020
Cited alongside, same era.
R. Li, R. T. Tan, and L.-F. Cheong, “All in one bad weather removal using architectural search,” in CVPR , 2020
2020
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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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Y. Dong, Y. Liu, H. Zhang, S. Chen, and Y. Qiao, “Fd-gan: Generative adversarial networks with fusion-discriminator for single image dehazing,” in AAAI , 2020
Y. Zhou, Z. Chen, P. Li, H. Song, C. P. Chen, and B. Sheng, “Fsad-net: feedback spatial attention dehazing network,” IEEE transactions on neural networks and learning systems , vol. 34, no. 10, pp. 7719–7733, 2022
2022
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F.-J. Tsai, Y.-T. Peng, Y.-Y. Lin, C.-C. Tsai, and C.-W. Lin, “Stripformer: Strip transformer for fast image deblurring,” in ECCV , 2022
2022
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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 CVPR , 2022
2022
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Z. Wang, X. Cun, J. Bao, W. Zhou, J. Liu, and H. Li, “Uformer: A general u-shaped transformer for image restoration,” in CVPR , 2022
2022
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C. Si, W. Yu, P. Zhou, Y. Zhou, X. Wang, and S. YAN, “Inception transformer,” in NeurIPS , 2022
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2020
Cited alongside, same era.
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 CVPR , 2020
2020
Cited alongside, same era.
Y. Gou, B. Li, Z. Liu, S. Yang, and X. Peng, “Clearer: Multi-scale neural architecture search for image restoration,” in NeurIPS , 2020
2020
Cited alongside, same era.
K. Xu, M. Qin, F. Sun, Y. Wang, Y.-K. Chen, and F. Ren, “Learning in the frequency domain,” in CVPR , 2020
2020
Cited alongside, same era.
C. Tian, Y. Xu, and W. Zuo, “Image denoising using deep cnn with batch renormalization,” Neural Networks , vol. 121, pp. 461–473, 2020
2020
Cited alongside, same era.
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, and L. Shao, “Learning enriched features for real image restoration and enhancement,” in ECCV , 2020
2020
Cited alongside, same era.
Z. Shi, Y. Chen, E. Gavves, P. Mettes, and C. G. Snoek, “Unsharp mask guided filtering,” IEEE Transactions on Image Processing , vol. 30, pp. 7472–7485, 2021
2021
Cited alongside, same era.
C. Chen and H. Li, “Robust representation learning with feedback for single image deraining,” in CVPR , 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
Z. Chen, Y. Zhang, J. Gu, y. zhang, L. Kong, and X. Yuan, “Cross aggregation transformer for image restoration,” in NeurIPS , 2022
2022
Later among the works it cites.
N. Park and S. Kim, “How do vision transformers work?” in ICLR , 2022
2022
Later among the works it cites.
P. Wang, W. Zheng, T. Chen, and Z. Wang, “Anti-oversmoothing in deep vision transformers via the fourier domain analysis: From theory to practice,” in ICLR , 2022
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 ECCV , 2022
2022
Later among the works it cites.
X. Chu, L. Chen, and W. Yu, “Nafssr: Stereo image super-resolution using nafnet,” in CVPR Workshops , 2022
2022
Later among the works it cites.
C. Zhang, Y. Zhu, Q. Yan, J. Sun, and Y. Zhang, “All-in-one multi-degradation image restoration network via hierarchical degradation representation,” in ACM MM , 2023
2023
Later among the works it cites.
V. Potlapalli, S. W. Zamir, S. Khan, and F. Khan, “Promptir: Prompting for all-in-one image restoration,” in NeurIPS , 2023
2023
Later among the works it cites.
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 CVPR , 2023
2023
Later among the works it cites.
J. Zhang, J. Huang, M. Yao, Z. Yang, H. Yu, M. Zhou, and F. Zhao, “Ingredient-oriented multi-degradation learning for image restoration,” in CVPR , 2023
2023
Later among the works it cites.
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 CVPR , 2023
2023
Later among the works it cites.
Y. Cui, Y. Tao, W. Ren, and A. Knoll, “Dual-domain attention for image deblurring,” in AAAI , 2023
2023
Later among the works it cites.
J. Zhang, Y. Zhang, J. Gu, Y. Zhang, L. Kong, and X. Yuan, “Accurate image restoration with attention retractable transformer,” in ICLR , 2023
2023
Later among the works it cites.
Y. Liu, X. Chen, X. Ma, X. Wang, J. Zhou, Y. Qiao, and C. Dong, “Unifying image processing as visual prompting question answering,” in ICML , 2023
2023
Later among the works it cites.
Y. Cui, Y. Tao, Z. Bing, W. Ren, X. Gao, X. Cao, K. Huang, and A. Knoll, “Selective frequency network for image restoration,” in ICLR , 2023
2023
Later among the works it cites.
W.-Y. Hsu and P.-W. Jian, “Wavelet pyramid recurrent structure-preserving attention network for single image super-resolution,” IEEE Transactions on Neural Networks and Learning Systems , 2023
2023
Later among the works it cites.
J. Zhang, J. Huang, M. Yao, Z. Yang, H. Yu, M. Zhou, and F. Zhao, “Ingredient-oriented multi-degradation learning for image restoration,” in CVPR , 2023
2023
Later among the works it cites.
Y. Ai, H. Huang, X. Zhou, J. Wang, and R. He, “Multimodal prompt perceiver: Empower adaptiveness generalizability and fidelity for all-in-one image restoration,” in CVPR , 2024
2024
Closest in time.
M. V. Conde, G. Geigle, and R. Timofte, “Instructir: High-quality image restoration following human instructions,” in ECCV , 2024
2024
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Y. Jiang, Z. Zhang, T. Xue, and J. Gu, “Autodir: Automatic all-in-one image restoration with latent diffusion,” in ECCV , 2024
2024
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P. Liang, J. Jiang, X. Liu, and J. Ma, “Image deblurring by exploring in-depth properties of transformer,” IEEE Transactions on Neural Networks and Learning Systems , 2024
2024
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Z. Luo, F. K. Gustafsson, Z. Zhao, J. Sjölund, and T. B. Schön, “Controlling vision-language models for universal image restoration,” in ICLR , 2024
2024
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H. Yang, L. Pan, Y. Yang, and W. Liang, “Language-driven all-in-one adverse weather removal,” in CVPR , 2024
2024
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T. Wang, K. Zhang, Z. Shao, W. Luo, B. Stenger, T. Lu, T.-K. Kim, W. Liu, and H. Li, “Gridformer: Residual dense transformer with grid structure for image restoration in adverse weather conditions,” International Journal of Computer Vision , pp. 1–23, 2024
2024
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A. Zafar, D. Aftab, R. Qureshi, X. Fan, P. Chen, J. Wu, H. Ali, S. Nawaz, S. Khan, and M. Shah, “Single stage adaptive multi-attention network for image restoration,” IEEE Transactions on Image Processing , 2024
2024
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