Fetching the paper…
Reading the bibliography…
Recent years have seen significant advancements in image restoration, largely attributed to the development of modern deep neural networks, such as CNNs and Transformers.
Kalman, R.E.: A new approach to linear filtering and prediction problems (1960)
1960
Earlier work this paper cites.
Charbonnier, P., Blanc-Feraud, L., Aubert, G., Barlaud, M.: Two deterministic half-quadratic regularization algorithms for computed imaging. In: Proceedings of 1st international conference on image processing. vol. 2, pp. 168–172. IEEE (1994)
1994
Earlier work this paper cites.
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001. vol. 2, pp. 416–423. IEEE (2001)
2001
Earlier work this paper cites.
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE transactions on pattern analysis and machine intelligence 33
2010
Earlier work this paper cites.
Zhang, L., Wu, X., Buades, A., Li, X.: Color demosaicking by local directional interpolation and nonlocal adaptive thresholding. Journal of Electronic imaging 20
2011
Earlier work this paper cites.
Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding (2012)
2012
Earlier work this paper cites.
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Curves and Surfaces: 7th International Conference, Avignon, France, June 24-30, 2010, Revised Selected Papers 7. pp. 711–730. Springer (2012)
2012
Earlier work this paper cites.
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV 13. pp. 184–199. Springer (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Dong, C., Deng, Y., Loy, C.C., Tang, X.: Compression artifacts reduction by a deep convolutional network. In: Proceedings of the IEEE international conference on computer vision. pp. 576–584 (2015)
2015
Earlier work this paper cites.
Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5197–5206 (2015)
2015
Earlier work this paper cites.
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1646–1654 (2016)
2016
Earlier work this paper cites.
Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
Ma, K., Duanmu, Z., Wu, Q., Wang, Z., Yong, H., Li, H., Zhang, L.: Waterloo exploration database: New challenges for image quality assessment models. IEEE Transactions on Image Processing 26
2016
Earlier work this paper cites.
Cavigelli, L., Hager, P., Benini, L.: Cas-cnn: A deep convolutional neural network for image compression artifact suppression. In: 2017 International Joint Conference on Neural Networks (IJCNN). pp. 752–759. IEEE (2017)
2017
Earlier work this paper cites.
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 624–632 (2017)
2017
Earlier work this paper cites.
Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 136–144 (2017)
2017
Earlier work this paper cites.
Matsui, Y., Ito, K., Aramaki, Y., Fujimoto, A., Ogawa, T., Yamasaki, T., Aizawa, K.: Sketch-based manga retrieval using manga109 dataset. Multimedia Tools and Applications 76
2017
Earlier work this paper cites.
Plotz, T., Roth, S.: Benchmarking denoising algorithms with real photographs. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1586–1595 (2017)
2017
Earlier work this paper cites.
Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L.: Ntire 2017 challenge on single image super-resolution: Methods and results. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 114–125 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing 26
2017
Earlier work this paper cites.
Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep cnn denoiser prior for image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3929–3938 (2017)
2017
Earlier work this paper cites.
Abdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1692–1700 (2018)
2018
Earlier work this paper cites.
Ahn, N., Kang, B., Sohn, K.A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European conference on computer vision (ECCV). pp. 252–268 (2018)
2018
Earlier work this paper cites.
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7132–7141 (2018)
2018
Earlier work this paper cites.
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Loy, C.C.: Esrgan: Enhanced super-resolution generative adversarial networks. In: The European Conference on Computer Vision Workshops (ECCVW) (September 2018)
2018
Earlier work this paper cites.
Zhang, K., Zuo, W., Zhang, L.: Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. IEEE Transactions on Image Processing 27
2018
Earlier work this paper cites.
Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European conference on computer vision (ECCV). pp. 286–301 (2018)
2018
Earlier work this paper cites.
Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2472–2481 (2018)
2018
Earlier work this paper cites.
Dai, T., Cai, J., Zhang, Y., Xia, S.T., Zhang, L.: Second-order attention network for single image super-resolution. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11065–11074 (2019)
2019
Earlier work this paper cites.
Fu, X., Zha, Z.J., Wu, F., Ding, X., Paisley, J.: Jpeg artifacts reduction via deep convolutional sparse coding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2501–2510 (2019)
2019
Cited alongside, same era.
Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight image super-resolution with information multi-distillation network. In: Proceedings of the 27th acm international conference on multimedia. pp. 2024–2032 (2019)
2019
Cited alongside, same era.
Gu, A., Dao, T., Ermon, S., Rudra, A., Ré, C.: Hippo: Recurrent memory with optimal polynomial projections. Advances in neural information processing systems 33
2020
Cited alongside, same era.
Ji, X., Cao, Y., Tai, Y., Wang, C., Li, J., Huang, F.: Real-world super-resolution via kernel estimation and noise injection. In: proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 466–467 (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 (CVPR). pp. 17683–17693 (June 2022)
2022
Later among the works it cites.
Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: Efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5728–5739 (2022)
2022
Later among the works it cites.
Zhang, X., Zeng, H., Guo, S., Zhang, L.: Efficient long-range attention network for image super-resolution. In: European Conference on Computer Vision. pp. 649–667. Springer (2022)
2022
Later among the works it cites.
Chen, X., Wang, X., Zhou, J., Qiao, Y., Dong, C.: Activating more pixels in image super-resolution transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22367–22377 (2023)
2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Li, W., Zhou, K., Qi, L., Jiang, N., Lu, J., Jia, J.: Lapar: Linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
Luo, X., Xie, Y., Zhang, Y., Qu, Y., Li, C., Fu, Y.: Latticenet: Towards lightweight image super-resolution with lattice block. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII 16. pp. 272–289. Springer (2020)
2020
Cited alongside, same era.
Mei, Y., Fan, Y., Zhou, Y., Huang, L., Huang, T.S., Shi, H.: Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5690–5699 (2020)
2020
Cited alongside, same era.
Niu, B., Wen, W., Ren, W., Zhang, X., Yang, L., Wang, S., Zhang, K., Cao, X., Shen, H.: Single image super-resolution via a holistic attention network. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16. pp. 191–207. Springer (2020)
2020
Cited alongside, same era.
Shazeer, N.: Glu variants improve transformer. arXiv preprint arXiv:2002.05202 (2020)
2020
Cited alongside, same era.
Zhou, S., Zhang, J., Zuo, W., Loy, C.C.: Cross-scale internal graph neural network for image super-resolution. In: Advances in Neural Information Processing Systems (2020)
2020
Cited alongside, same era.
Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 12299–12310 (2021)
2021
Cited alongside, same era.
Franzen, R.: Kodak lossless true color image suite (2021), http://r0k.us/graphics/kodak/
2021
Cited alongside, same era.
Later among the works it cites.
2023
Later among the works it cites.
Chen, Z., Zhang, Y., Gu, J., Kong, L., Yang, X., Yu, F.: Dual aggregation transformer for image super-resolution. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 12312–12321 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Islam, M.M., Hasan, M., Athrey, K.S., Braskich, T., Bertasius, G.: Efficient movie scene detection using state-space transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18749–18758 (2023)
2023
Later among the works it cites.
Li, Y., Fan, Y., Xiang, X., Demandolx, D., Ranjan, R., Timofte, R., Van Gool, L.: Efficient and explicit modelling of image hierarchies for image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18278–18289 (2023)
2023
Later among the works it cites.
Sun, L., Dong, J., Tang, J., Pan, J.: Spatially-adaptive feature modulation for efficient image super-resolution. In: ICCV (2023)
2023
Later among the works it cites.
Wang, J., Zhu, W., Wang, P., Yu, X., Liu, L., Omar, M., Hamid, R.: Selective structured state-spaces for long-form video understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6387–6397 (2023)
2023
Later among the works it cites.
Zha, Y., Wang, J., Dai, T., Chen, B., Wang, Z., Xia, S.T.: Instance-aware dynamic prompt tuning for pre-trained point cloud models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14161–14170 (2023)
2023
Later among the works it cites.
Zhang, J., Zhang, Y., Gu, J., Zhang, Y., Kong, L., Yuan, X.: Accurate image restoration with attention retractable transformer. In: ICLR (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Bai, J., Gao, K., Min, S., Xia, S.T., Li, Z., Liu, W.: Badclip: Trigger-aware prompt learning for backdoor attacks on clip. In: CVPR (2024)
2024
Closest in time.
Gao, K., Bai, Y., Gu, J., Xia, S.T., Torr, P., Li, Z., Liu, W.: Inducing high energy-latency of large vision-language models with verbose images. In: ICLR (2024)
2024
Closest in time.
2024
Closest in time.
Hu, V.T., Baumann, S.A., Gui, M., Grebenkova, O., Ma, P., Fischer, J., Ommer, B.: Zigma: A dit-style zigzag mamba diffusion model. In: ECCV (2024)
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Zha, Y., Ji, H., Li, J., Li, R., Dai, T., Chen, B., Wang, Z., Xia, S.T.: Towards compact 3d representations via point feature enhancement masked autoencoders. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 6962–6970 (2024)
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Zhang, T., He, S., Dai, T., Wang, Z., Chen, B., Xia, S.T.: Vision-language pre-training with object contrastive learning for 3d scene understanding. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 7296–7304 (2024)
2024
Closest in time.