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Video denoising aims to recover high-quality frames from the noisy video.
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2018
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P. Arias and J.-M. Morel, “Video denoising via empirical bayesian estimation of space-time patches,” Journal of Mathematical Imaging and Vision , vol. 60, no. 1, pp. 70–93, 2018
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2019
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M. Claus and J. van Gemert, “Videnn: Deep blind video denoising,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2019, pp. 1843–1852. 0
2019
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M. Tassano, J. Delon, and T. Veit, “Dvdnet: A fast network for deep video denoising,” in 2019 IEEE International Conference on Image Processing (ICIP) , 2019, pp. 1805–1809
2019
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2019
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S. Cheng, Y. Wang, H. Huang, D. Liu, H. Fan, and S. Liu, “Nbnet: Noise basis learning for image denoising with subspace projection,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . online: IEEE, 2021, pp. 4894–4904
2021
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T. Ehret, A. Davy, J.-M. Morel, G. Facciolo, and P. Arias, “Model-blind video denoising via frame-to-frame training,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 11 361–11 370
2019
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S. Laine, T. Karras, J. Lehtinen, and T. Aila, High-Quality Self-Supervised Deep Image Denoising . Red Hook, NY, USA: Curran Associates Inc., 2019
2019
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P. Ramachandran, N. Parmar, A. Vaswani, I. Bello, A. Levskaya, and J. Shlens, “Stand-alone self-attention in vision models,” Advances in Neural Information Processing Systems , vol. 32, 2019
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2020
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2020
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M. Tassano, J. Delon, and T. Veit, “Fastdvdnet: Towards real-time deep video denoising without flow estimation,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 1351–1360
2020
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2020
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2021
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2021
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2021
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2022
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S. Yu, B. Park, and J. Jeong, “Deep iterative down-up cnn for image denoising,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , 2019, pp. 2095–2103
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