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The use of a single image restoration framework to achieve multi-task image restoration has garnered significant attention from researchers.
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B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng, “Aod-net: All-in-one dehazing network,” in 2017 IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 4780–4788
2017
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K. Zhang, W. Zuo, S. Gu, and L. Zhang, “Learning deep cnn denoiser prior for image restoration,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 2808–2817
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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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019, pp. 3937–3946
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 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 3843–3851
2019
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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, 2019
2019
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Y. Qu, Y. Chen, J. Huang, and Y. Xie, “Enhanced pix2pix dehazing network,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 8152–8160
2019
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R. Yasarla and V. M. Patel, “Uncertainty guided multi-scale residual learning-using a cycle spinning cnn for single image de-raining,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 8397–8406
2019
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W. Wei, D. Meng, Q. Zhao, Z. Xu, and Y. Wu, “Semi-supervised transfer learning for image rain removal,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 3872–3881
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 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 3843–3851
2019
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H. Li, X. He, Z. Yu, and J. Luo, “Noise-robust image fusion with low-rank sparse decomposition guided by external patch prior,” Information Sciences , vol. 532, pp. 14–37, 2020
2020
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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 (CVPR) , June 2020, pp. 8346–8355
2020
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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, 2020
2020
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2020
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X. Qin, Z. Wang, Y. Bai, X. Xie, and H. Jia, “Ffa-net: Feature fusion attention network for single image dehazing,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, pp. 11 908–11 915, 2020
2020
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2020
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2020
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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, 2020
2020
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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 22020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 8343–8352
2020
Cited alongside, same era.
J. Pang, D. Zhang, H. Li, W. Liu, and Z. Yu, “Hazy re-id: An interference suppression model for domain adaptation person re-identification under inclement weather condition,” in 2021 IEEE International Conference on Multimedia and Expo (ICME) , 2021
2023
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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 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 5815–5824
2023
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Y. Zhu, T. Wang, X. Fu, X. Yang, X. Guo, J. Dai, Y. Qiao, and X. Hu, “Learning weather-general and weather-specific features for image restoration under multiple adverse weather conditions,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 21 747–21 758
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2021
Cited alongside, same era.
S.-J. Cho, S.-W. Ji, J.-P. Hong, S.-W. Jung, and S.-J. Ko, “Rethinking coarse-to-fine approach in single image deblurring,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 4641–4650
2021
Cited alongside, same era.
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 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 12 294–12 305
2021
Cited alongside, same era.
H. Li, Y. Cen, Y. Liu, X. Chen, and Z. Yu, “Different input resolutions and arbitrary output resolution: A meta learning-based deep framework for infrared and visible image fusion,” IEEE Transactions on Image Processing , vol. 30, pp. 4070–4083, 2021
2021
Cited alongside, same era.
D.-W. Jaw, S.-C. Huang, and S.-Y. Kuo, “Desnowgan: An efficient single image snow removal framework using cross-resolution lateral connection and gans,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 4, pp. 1342–1350, 2021
2021
Cited alongside, same era.
W. Chen, H. Fang, C. Hsieh, C. Tsai, I.-H. Chen, J. Ding, and S. Kuo, “All snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , vol. 34, no. 10, 2021, pp. 4196–4205
2021
Cited alongside, same era.
J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, and R. Timofte, “Swinir: Image restoration using swin transformer,” in 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) , 2021, pp. 1833–1844
2021
Cited alongside, same era.
Q. Fan, D. Chen, L. Yuan, G. Hua, N. Yu, and B. Chen, “A general decoupled learning framework for parameterized image operators,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 1, pp. 33–47, 2021
2021
Cited alongside, same era.
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, and L. Shao, “Multi-stage progressive image restoration,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 14 816–14 826
2021
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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,” in Advances in Neural Information Processing Systems (NeruIPS) , vol. 36, 2023, pp. 71 275–71 293
2023
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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 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 5825–5835
2023
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Y. Cui, Y. Tao, Z. Bing, W. Ren, X. Gao, X. Cao, K. Huang, and A. Knoll, “Selective frequency network for image restoration,” in The Eleventh International Conference on Learning Representations , 2023
2023
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S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, and L. Shao, “Learning enriched features for fast image restoration and enhancement,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 2, pp. 1934–1948, 2023
2023
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J. Chen, L. Yang, W. Liu, X. Tian, and J. Ma, “Lenfusion: A joint low-light enhancement and fusion network for nighttime infrared and visible image fusion,” IEEE Transactions on Instrumentation and Measurement , vol. 73, pp. 1–15, 2024
2024
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H. Li, J. Liu, Y. Zhang, and Y. Liu, “A deep learning framework for infrared and visible image fusion without strict registration,” International Journal of Computer Vision , vol. 132, p. 1625–1644, 2024
2024
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H. Li, Q. Hu, and Z. Hu, “Catalyst for clustering-based unsupervised object re-identification: Feature calibration,” in The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI) , vol. 38, no. 4, 2024, pp. 3091–3099
2024
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H. Gupta, O. Kotlyar, H. Andreasson, and A. J. Lilienthal, “Robust object detection in challenging weather conditions,” in 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , 2024, pp. 7508–7517
2024
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H. Zhang, L. Xiao, X. Cao, and H. Foroosh, “Multiple adverse weather conditions adaptation for object detection via causal intervention,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 3, pp. 1742–1756, 2024
2024
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S. Ding, Q. Wang, L. Guo, X. Li, L. Ding, and X. Wu, “Wavelet and adaptive coordinate attention guided fine-grained residual network for image denoising,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 7, pp. 6156–6166, 2024
2024
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Z. Liu, J. Wu, G. Shi, W. Yang, W. Dong, and Q. Zhao, “Motion-oriented hybrid spiking neural networks for event-based motion deblurring,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 5, pp. 3742–3754, 2024
2024
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Y. Zhang, S. Zhou, and H. Li, “Depth information assisted collaborative mutual promotion network for single image dehazing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 2846–2855
2024
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J. Lin, Z. Zhang, Y. Wei, D. Ren, D. Jiang, Q. Tian, and W. Zuo, “Improving image restoration through removing degradations in textual representations,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024, pp. 2866–2878
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Z. Tan, Y. Wu, Q. Liu, Q. Chu, L. Lu, J. Ye, and N. Yu, “Exploring the application of large-scale pre-trained models on adverse weather removal,” IEEE Transactions on Image Processing , vol. 33, pp. 1683–1698, 2024
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H. Li, M. Yuan, J. Li, Y. Liu, G. Lu, Y. Xu, Z. Yu, and D. Zhang, “Focus affinity perception and super-resolution embedding for multifocus image fusion,” IEEE Transactions on Neural Networks and Learning Systems , 2024, dOI: 10.1109/TNNLS.2024.3367782
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H. Yang, L. Pan, Y. Yang, and W. Liang, “Language-driven all-in-one adverse weather removal,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024, pp. 24 902–24 912
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