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Image restoration is a classic low-level problem aimed at recovering high-quality images from low-quality images with various degradations such as blur, noise, rain, haze, etc.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, no. 4, pp. 600–612, 2004
2004
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2013
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
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2014
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L. Zhang, L. Zhang, and A. C. Bovik, “A feature-enriched completely blind image quality evaluator,” IEEE Transactions on Image Processing , vol. 24, no. 8, pp. 2579–2591, 2015
2015
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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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E. Agustsson and R. Timofte, “Ntire 2017 challenge on single image super-resolution: Dataset and study,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2017, pp. 126–135
2017
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R. Timofte, E. Agustsson, L. Van Gool, M.-H. Yang, and L. Zhang, “Ntire 2017 challenge on single image super-resolution: Methods and results,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2017, pp. 114–125
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 Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3855–3863
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 Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3883–3891
2017
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M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
2017
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2017
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O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: Blind motion deblurring using conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8183–8192
2018
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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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R. Qian, R. T. Tan, W. Yang, J. Su, and J. Liu, “Attentive generative adversarial network for raindrop removal from a single image,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2482–2491
2018
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Y.-F. Liu, D.-W. Jaw, S.-C. Huang, and J.-N. Hwang, “Desnownet: Context-aware deep network for snow removal,” IEEE Transactions on Image Processing , vol. 27, no. 6, pp. 3064–3073, 2018
2018
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R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 586–595
2018
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O. Kupyn, T. Martyniuk, J. Wu, and Z. Wang, “Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 8878–8887
2019
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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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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
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K. Ding, K. Ma, S. Wang, and E. P. Simoncelli, “Image quality assessment: Unifying structure and texture similarity,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 5, pp. 2567–2581, 2020
2020
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
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L. Wang, Y. Wang, X. Dong, Q. Xu, J. Yang, W. An, and Y. Guo, “Unsupervised degradation representation learning for blind super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 581–10 590
2021
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P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” Advances in neural information processing systems , vol. 34, pp. 8780–8794, 2021
2021
Cited alongside, same era.
J. Ke, Q. Wang, Y. Wang, P. Milanfar, and F. Yang, “Musiq: Multi-scale image quality transformer,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 5148–5157
2021
Cited alongside, same era.
Y. Liang, S. Anwar, and Y. Liu, “Drt: A lightweight single image deraining recursive transformer,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 589–598
2022
Cited alongside, same era.
L. Chen, X. Chu, X. Zhang, and J. Sun, “Simple baselines for image restoration,” in European conference on computer vision . Springer, 2022, pp. 17–33
2022
Cited alongside, same era.
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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5825–5835
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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 747–21 758
2023
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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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 5728–5739
2022
Cited alongside, same era.
B. Li, X. Liu, P. Hu, Z. Wu, J. Lv, and X. Peng, “All-in-one image restoration for unknown corruption,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 452–17 462
2022
Cited alongside, same era.
J. M. J. Valanarasu, R. Yasarla, and V. M. Patel, “Transweather: Transformer-based restoration of images degraded by adverse weather conditions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2353–2363
2022
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Cited alongside, same era.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in neural information processing systems , vol. 35, pp. 36 479–36 494, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
H. Chung, B. Sim, D. Ryu, and J. C. Ye, “Improving diffusion models for inverse problems using manifold constraints,” Advances in Neural Information Processing Systems , vol. 35, pp. 25 683–25 696, 2022
2022
Cited alongside, same era.
H. Li, Y. Yang, M. Chang, S. Chen, H. Feng, Z. Xu, Q. Li, and Y. Chen, “Srdiff: Single image super-resolution with diffusion probabilistic models,” Neurocomputing , vol. 479, pp. 47–59, 2022
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
B. Fei, Z. Lyu, L. Pan, J. Zhang, W. Yang, T. Luo, B. Zhang, and B. Dai, “Generative diffusion prior for unified image restoration and enhancement,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9935–9946
2023
Later among the works it cites.
O. Özdenizci and R. Legenstein, “Restoring vision in adverse weather conditions with patch-based denoising diffusion models,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
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2023
Later among the works it cites.
2023
Later among the works it cites.
2023
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2023
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J. Wang, K. C. Chan, and C. C. Loy, “Exploring clip for assessing the look and feel of images,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 2555–2563
2023
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L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3836–3847
2023
Later among the works it cites.
2023
Later among the works it cites.
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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 747–21 758
2023
Later among the works it cites.
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 , 2024
2024
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Y. Wang, X. Yan, F. L. Wang, H. Xie, W. Yang, X.-P. Zhang, J. Qin, and M. Wei, “Ucl-dehaze: Towards real-world image dehazing via unsupervised contrastive learning,” IEEE Transactions on Image Processing , 2024
2024
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2024
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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. 36, 2024
2024
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2024
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2024
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2024
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W. Zhao, L. Bai, Y. Rao, J. Zhou, and J. Lu, “Unipc: A unified predictor-corrector framework for fast sampling of diffusion models,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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