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Image Restoration has seen remarkable progress in recent years.
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C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al., Photo-realistic single image super-resolution using a generative adversarial network, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4681–4690
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R. Timofte, E. Agustsson, L. Van Gool, M.-H. Yang, 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
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E. Agustsson, 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
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M. Fritsche, S. Gu, R. Timofte, Frequency separation for real-world super-resolution, in: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), IEEE, 2019, pp. 3599–3608
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S.-Y. Wang, O. Wang, R. Zhang, A. Owens, A. A. Efros, Cnn-generated images are surprisingly easy to spot… for now, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 8695–8704
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S. Jung, M. Keuper, Spectral distribution aware image generation, in: Proceedings of the AAAI conference on artificial intelligence, Vol. 35, 2021, pp. 1734–1742
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doi:https://doi.org/10.1016/j.patcog.2022.108909
H. Shen, Z.-Q. Zhao, W. Liao, W. Tian, D.-S. Huang, Joint operation and attention block search for lightweight image restoration, Pattern Recognition 132 (2022) 108909 · 2022
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M. Tancik, P. Srinivasan, B. Mildenhall, S. Fridovich-Keil, N. Raghavan, U. Singhal, R. Ramamoorthi, J. Barron, R. Ng, Fourier features let networks learn high frequency functions in low dimensional domains, Advances in Neural Information Processing Systems 33 (2020) 7537–7547
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R. Durall, M. Keuper, J. Keuper, Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 7890–7899
2020
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Y. Zhou, W. Deng, T. Tong, 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
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J. Ho, A. Jain, P. Abbeel, Denoising diffusion probabilistic models, Advances in Neural Information Processing Systems 33 (2020) 6840–6851
2020
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S. Zhai, C. Ren, Z. Wang, X. He, L. Qing, An effective deep network using target vector update modules for image restoration, Pattern Recognition 122 (2022) 108333 · 2021
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J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, R. Timofte, Swinir: Image restoration using swin transformer, in: Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 1833–1844
2021
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L. Jiang, B. Dai, W. Wu, C. C. Loy, Focal frequency loss for image reconstruction and synthesis, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 13919–13929
2021
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Later among the works it cites.
J. Gu, H. Cai, C. Dong, J. S. Ren, R. Timofte, Y. Gong, S. Lao, S. Shi, J. Wang, S. Yang, et al., Ntire 2022 challenge on perceptual image quality assessment, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 951–967
2022
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L. Wang, Y. Guo, Y. Wang, J. Li, S. Gu, R. Timofte, L. Chen, X. Chu, W. Yu, K. Jin, et al., Ntire 2022 challenge on stereo image super-resolution: Methods and results, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 906–919
2022
Later among the works it cites.
T. Liu, S. Tan, Decoupled frequency learning for dynamic scene deblurring, in: 2022 26th International Conference on Pattern Recognition (ICPR), IEEE, 2022, pp. 89–96
2022
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A. Farshad, Y. Yeganeh, P. Gehlbach, N. Navab, Y-net: A spatiospectral dual-encoder network for medical image segmentation, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part II, Springer, 2022, pp. 582–592
2022
Later among the works it cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, 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. 10684–10695
2022
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C. Saharia, J. Ho, W. Chan, T. Salimans, D. J. Fleet, M. Norouzi, Image super-resolution via iterative refinement, IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)
2022
Later among the works it cites.
doi:https://doi.org/10.1016/j.patcog.2023.109602
F. Zhou, X. Sun, J. Dong, X. X. Zhu, Surroundnet: Towards effective low-light image enhancement, Pattern Recognition 141 (2023) 109602 · 2023
Closest in time.
doi:https://doi.org/10.1016/j.patcog.2023.109603
R. K. Thakur, S. K. Maji, Multi scale pixel attention and feature extraction based neural network for image denoising, Pattern Recognition 141 (2023) 109603 · 2023
Closest in time.
M. V. Conde, U.-J. Choi, M. Burchi, R. Timofte, Swin2sr: Swinv2 transformer for compressed image super-resolution and restoration, in: Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part II, Springer, 2023, pp. 669–687
2023
Closest in time.