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Current video deblurring methods have limitations in recovering high-frequency information since the regression losses are conservative with high-frequency details.
Cho, S., Wang, J., Lee, S.: Video deblurring for hand-held cameras using patch-based synthesis. ACM Transactions on Graphics (TOG) 31
2012
Earlier work this paper cites.
Li, W., Zhao, L., Xu, D., Lu, D.: Efficient image completion method based on alternating direction theory. In: 2013 IEEE International Conference on Image Processing. pp. 700–703. IEEE (2013)
2013
Earlier work this paper cites.
Hyun Kim, T., Mu Lee, K.: Generalized video deblurring for dynamic scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5426–5434 (2015)
2015
Earlier work this paper cites.
Li, W., Zhao, L., Lin, Z., Xu, D., Lu, D.: Non-local image inpainting using low-rank matrix completion. In: Computer Graphics Forum. vol. 34, pp. 111–122. Wiley Online Library (2015)
2015
Earlier work this paper cites.
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
2016
Earlier work this paper cites.
Huang, H., He, R., Sun, Z., Tan, T.: Wavelet-srnet: A wavelet-based cnn for multi-scale face super resolution. In: Proceedings of the IEEE international conference on computer vision. pp. 1689–1697 (2017)
2017
Earlier work this paper cites.
Hyun Kim, T., Mu Lee, K., Scholkopf, B., Hirsch, M.: Online video deblurring via dynamic temporal blending network. In: Proceedings of the IEEE international conference on computer vision. pp. 4038–4047 (2017)
2017
Earlier work this paper cites.
Nah, S., Hyun Kim, T., Mu Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3883–3891 (2017)
2017
Earlier work this paper cites.
Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich, W., Wang, O.: Deep video deblurring for hand-held cameras. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1279–1288 (2017)
2017
Earlier work this paper cites.
Zhang, K., Luo, W., Zhong, Y., Ma, L., Liu, W., Li, H.: Adversarial spatio-temporal learning for video deblurring. IEEE Transactions on Image Processing 28
2018
Earlier work this paper cites.
Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8102–8111 (2019)
2019
Earlier work this paper cites.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32
2019
Earlier work this paper cites.
Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: Edvr: Video restoration with enhanced deformable convolutional networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 0–0 (2019)
2019
Earlier work this paper cites.
Zhou, S., Zhang, J., Pan, J., Xie, H., Zuo, W., Ren, J.: Spatio-temporal filter adaptive network for video deblurring. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 2482–2491 (2019)
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Pan, J., Bai, H., Tang, J.: Cascaded deep video deblurring using temporal sharpness prior. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 3043–3051 (2020)
2020
Cited alongside, same era.
Xiang, X., Wei, H., Pan, J.: Deep video deblurring using sharpness features from exemplars. IEEE Transactions on Image Processing 29
2020
Cited alongside, same era.
Zhao, L., Mo, Q., Lin, S., Wang, Z., Zuo, Z., Chen, H., Xing, W., Lu, D.: Uctgan: Diverse image inpainting based on unsupervised cross-space translation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5741–5750 (2020)
2020
Cited alongside, same era.
Zheng, B., Yuan, S., Slabaugh, G., Leonardis, A.: Image demoireing with learnable bandpass filters. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3636–3645 (2020)
2020
Cited alongside, same era.
Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: A general u-shaped transformer for image restoration. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 17683–17693 (2022)
2022
Later among the works it cites.
Blattmann, A., Rombach, R., Ling, H., Dockhorn, T., Kim, S.W., Fidler, S., Kreis, K.: Align your latents: High-resolution video synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22563–22575 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Chu, T., Chen, J., Sun, J., Lian, S., Wang, Z., Zuo, Z., Zhao, L., Xing, W., Lu, D.: Rethinking fast fourier convolution in image inpainting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 23195–23205 (2023)
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Zhong, Z., Gao, Y., Zheng, Y., Zheng, B.: Efficient spatio-temporal recurrent neural network for video deblurring. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16. pp. 191–207. Springer (2020)
2020
Cited alongside, same era.
Fan, Y., Hong, C., Wang, X., Zeng, Z., Guo, Z.: Multi-input-output fusion attention module for deblurring networks. In: 2021 IEEE international conference on big data (Big Data). pp. 3176–3182. IEEE (2021)
2021
Cited alongside, same era.
Li, D., Xu, C., Zhang, K., Yu, X., Zhong, Y., Ren, W., Suominen, H., Li, H.: Arvo: Learning all-range volumetric correspondence for video deblurring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7721–7731 (2021)
2021
Cited alongside, same era.
Suin, M., Rajagopalan, A.: Gated spatio-temporal attention-guided video deblurring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7802–7811 (2021)
2021
Cited alongside, same era.
Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34
2021
Cited alongside, same era.
Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., Shao, L.: Multi-stage progressive image restoration. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 14821–14831 (2021)
2021
Cited alongside, same era.
2022
Cited alongside, same era.
Chen, L., Chu, X., Zhang, X., Sun, J.: Simple baselines for image restoration. In: European Conference on Computer Vision. pp. 17–33. Springer (2022)
2022
Cited alongside, same era.
2023
Later among the works it cites.
Esser, P., Chiu, J., Atighehchian, P., Granskog, J., Germanidis, A.: Structure and content-guided video synthesis with diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7346–7356 (2023)
2023
Later among the works it cites.
Li, A., Zhao, L., Zuo, Z., Wang, Z., Xing, W., Lu, D.: Migt: Multi-modal image inpainting guided with text. Neurocomputing 520
2023
Later among the works it cites.
Liu, X., Park, D.H., Azadi, S., Zhang, G., Chopikyan, A., Hu, Y., Shi, H., Rohrbach, A., Darrell, T.: More control for free! image synthesis with semantic diffusion guidance. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 289–299 (January 2023)
2023
Later among the works it cites.
Luo, Z., Chen, D., Zhang, Y., Huang, Y., Wang, L., Shen, Y., Zhao, D., Zhou, J., Tan, T.: Videofusion: Decomposed diffusion models for high-quality video generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10209–10218 (2023)
2023
Later among the works it cites.
Özdenizci, O., Legenstein, R.: Restoring vision in adverse weather conditions with patch-based denoising diffusion models. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)
2023
Later among the works it cites.
Pan, J., Xu, B., Dong, J., Ge, J., Tang, J.: Deep discriminative spatial and temporal network for efficient video deblurring. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22191–22200 (2023)
2023
Later among the works it cites.
Ruan, L., Ma, Y., Yang, H., He, H., Liu, B., Fu, J., Yuan, N.J., Jin, Q., Guo, B.: Mm-diffusion: Learning multi-modal diffusion models for joint audio and video generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10219–10228 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Yu, S., Sohn, K., Kim, S., Shin, J.: Video probabilistic diffusion models in projected latent space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 18456–18466 (June 2023)
2023
Later among the works it cites.
Zuo, Z., Zhao, L., Li, A., Wang, Z., Zhang, Z., Chen, J., Xing, W., Lu, D.: Generative image inpainting with segmentation confusion adversarial training and contrastive learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 37, pp. 3888–3896 (2023)
2023
Later among the works it cites.
Li, G., Rao, C., Mo, J., Zhang, Z., Xing, W., Zhao, L.: Rethinking diffusion model for multi-contrast mri super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11365–11374 (2024)
2024
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Zhang, Z., Zhang, Q., Xing, W., Li, G., Zhao, L., Sun, J., Lan, Z., Luan, J., Huang, Y., Lin, H.: Artbank: Artistic style transfer with pre-trained diffusion model and implicit style prompt bank. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 7396–7404 (2024)
2024
Closest in time.