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Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts.
X. Wang, L. Xie, C. Dong, and Y. Shan, “Real-esrgan: Training real-world blind super-resolution with pure synthetic data,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 1905–1914
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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 (TIP) , vol. 13, no. 4, pp. 600–612, 2004
2004
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G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database forstudying face recognition in unconstrained environments,” in Workshop on faces in ’Real-Life’ Images: detection, alignment, and recognition , 2008
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
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W. Dong, L. Zhang, G. Shi, and X. Li, “Nonlocally centralized sparse representation for image restoration,” IEEE transactions on Image Processing , vol. 22, no. 4, pp. 1620–1630, 2012
2012
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S. Gu, W. Zuo, Q. Xie, D. Meng, X. Feng, and L. Zhang, “Convolutional sparse coding for image super-resolution,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1823–1831
2015
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S. Yang, P. Luo, C.-C. Loy, and X. Tang, “Wider face: A face detection benchmark,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 5525–5533
2016
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S. Gu, Q. Xie, D. Meng, W. Zuo, X. Feng, and L. Zhang, “Weighted nuclear norm minimization and its applications to low level vision,” International journal of computer vision , vol. 121, pp. 183–208, 2017
2017
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W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang, “Deep laplacian pyramid networks for fast and accurate super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 624–632
2017
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M. S. Sajjadi, B. Scholkopf, and M. Hirsch, “Enhancenet: Single image super-resolution through automated texture synthesis,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 4491–4500
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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2018
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K. Zhang, W. Zuo, and L. Zhang, “Learning a single convolutional super-resolution network for multiple degradations,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3262–3271
2018
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X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. Change Loy, “Esrgan: Enhanced super-resolution generative adversarial networks,” in Proceedings of the European conference on computer vision (ECCV) workshops , 2018, pp. 0–0
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/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 586–595
2018
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T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of GANs for improved quality, stability, and variation,” in Proceedings of International Conference on Learning Representations (ICLR) , 2018
2018
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J. Cai, H. Zeng, H. Yong, Z. Cao, and L. Zhang, “Toward real-world single image super-resolution: A new benchmark and a new model,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 3086–3095
2019
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T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 4401–4410
2019
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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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2020
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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 networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
2020
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A. Lugmayr, M. Danelljan, L. Van Gool, and R. Timofte, “Srflow: Learning the super-resolution space with normalizing flow,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16 . Springer, 2020, pp. 715–732
2020
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Y. Zhou, W. Deng, T. Tong, and Q. Gao, “Guided frequency separation network for real-world super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 428–429
2020
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X. Ji, Y. Cao, Y. Tai, C. Wang, J. Li, and F. Huang, “Real-world super-resolution via kernel estimation and noise injection,” in proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops , 2020, pp. 466–467
2020
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X. Li, C. Chen, S. Zhou, X. Lin, W. Zuo, and L. Zhang, “Blind face restoration via deep multi-scale component dictionaries,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2020
2020
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P. Wei, Z. Xie, H. Lu, Z. Zhan, Q. Ye, W. Zuo, and L. Lin, “Component divide-and-conquer for real-world image super-resolution,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VIII 16 . Springer, 2020, pp. 101–117
2020
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2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
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J. Liang, H. Zeng, and L. Zhang, “Details or artifacts: A locally discriminative learning approach to realistic image super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5657–5666
2022
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C. Chen, X. Shi, Y. Qin, X. Li, X. Han, T. Yang, and S. Guo, “Real-world blind super-resolution via feature matching with implicit high-resolution priors,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, pp. 1329–1338
2022
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S. Yang, T. Wu, S. Shi, S. Lao, Y. Gong, M. Cao, J. Wang, and Y. Yang, “Maniqa: Multi-dimension attention network for no-reference image quality assessment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 1191–1200
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K. Zhang, J. Liang, L. Van Gool, and R. Timofte, “Designing a practical degradation model for deep blind image super-resolution,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 4791–4800
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 Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 1833–1844
2021
Cited alongside, same era.
X. Pan, X. Zhan, B. Dai, D. Lin, C. C. Loy, and P. Luo, “Exploiting deep generative prior for versatile image restoration and manipulation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 11, pp. 7474–7489, 2021
2021
Cited alongside, same era.
K. C. Chan, X. Wang, X. Xu, J. Gu, and C. C. Loy, “Glean: Generative latent bank for large-factor image super-resolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 14 245–14 254
2021
Cited alongside, same era.
D. Fuoli, L. Van Gool, and R. Timofte, “Fourier space losses for efficient perceptual image super-resolution,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 2360–2369
2021
Cited alongside, same era.
C. Chen, X. Li, L. Yang, X. Lin, L. Zhang, and K.-Y. K. Wong, “Progressive semantic-aware style transformation for blind face restoration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
Cited alongside, same era.
X. Wang, Y. Li, H. Zhang, and Y. Shan, “Towards real-world blind face restoration with generative facial prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 9168–9178
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 (ICCV) , 2021, pp. 5148–5157
2021
Cited alongside, same era.
2022
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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
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2023
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2023
Later among the works it cites.
2023
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A. Niu, K. Zhang, T. X. Pham, J. Sun, Y. Zhu, I. S. Kweon, and Y. Zhang, “Cdpmsr: Conditional diffusion probabilistic models for single image super-resolution,” in 2023 IEEE International Conference on Image Processing (ICIP) . IEEE, 2023, pp. 615–619
2023
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S. Gao, X. Liu, B. Zeng, S. Xu, Y. Li, X. Luo, J. Liu, X. Zhen, and B. Zhang, “Implicit diffusion models for continuous super-resolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 10 021–10 030
2023
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2023
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2023
Later among the works it cites.
2023
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C.-H. Lin, J. Gao, L. Tang, T. Takikawa, X. Zeng, X. Huang, K. Kreis, S. Fidler, M.-Y. Liu, and T.-Y. Lin, “Magic3d: High-resolution text-to-3d content creation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 300–309
2023
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A. Hertz, K. Aberman, and D. Cohen-Or, “Delta denoising score,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2328–2337
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 , 2023
2023
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Y. Li, K. Zhang, J. Liang, J. Cao, C. Liu, R. Gong, Y. Zhang, H. Tang, Y. Liu, D. Demandolx et al. , “Lsdir: A large scale dataset for image restoration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1775–1787
2023
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Z. Yue, J. Wang, and C. C. Loy, “Resshift: Efficient diffusion model for image super-resolution by residual shifting,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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K. Zheng, C. Lu, J. Chen, and J. Zhu, “Dpm-solver-v3: Improved diffusion ode solver with empirical model statistics,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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2024
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2024
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Z. Wang, C. Lu, Y. Wang, F. Bao, C. Li, H. Su, and J. Zhu, “Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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R. Wu, T. Yang, L. Sun, Z. Zhang, S. Li, and L. Zhang, “Seesr: Towards semantics-aware real-world image super-resolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024, pp. 25 456–25 467
2024
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