The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Feedback Network for Image Super-Resolution
Open accessAbstract
Recent advances in image super-resolution (SR) explored the power of deep learning to achieve a better reconstruction performance. However, the feedback mechanism, which commonly exists in human visual system, has not been fully exploited in existing deep learning based image SR methods. In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information. Specifically, we use hidden states in a recurrent neural network (RNN) with constraints to achieve such feedback manner. A feedback block is designed to handle the feedback connections and to generate powerful high-level representations. The proposed SRFBN comes with a strong early reconstruction ability and can create the final high-resolution image step by step. In addition, we introduce a curriculum learning strategy to make the network well suitable for more complicated tasks, where the low-resolution images are corrupted by multiple types of degradation. Extensive experimental results demonstrate the superiority of the proposed SRFBN in comparison with the state-of-the-art methods. Code is avaliable at https://github.com/Paper99/SRFBN_CVPR19.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Self-citationDelving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1×
“Curriculum learning[2], which gradually increases the difficulty of the learned target, is well known as an efficient strategy to improve the training procedure.”
BackgroundLearning Deep CNN Denoiser Prior for Image Restoration
Zhang, K., Zuo, W., Gu, S., & Zhang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.300 · Cited by 2,116 · cited 2×
“Dataset Model Bicubic SRCNN VDSR IRCNN G IRCNN C SRMD(NF) RDN SRFBN SRFBN+ [7] [18] [43] [43] [44] [47] (Ours) (Ours) Set5 BD 28.”
BackgroundMethodCortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons
Hupé, J. M., James, A. C., Payne, B. R., Lomber, S. G., Girard, P., & Bullier, J. (1998) · Nature · doi:10.1038/29537 · Cited by 864
Cited by
Efficient Long-Range Attention Network for Image Super-Resolution
Zhang, X., Zeng, H., Guo, S., & Zhang, L. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19790-1_39 · Cited by 620
A Deep Journey into Super-resolution: A Survey
Anwar, S., Khan, S., & Barnes, N. (2021) · ACM Computing Surveys · doi:10.1145/3390462 · Cited by 394
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception
Jahidul Islam, M., Luo, P., & Sattar, J. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.018 · Cited by 269
MRI Super-Resolution With Partial Diffusion Models
Zhao, K., Pang, K., Hung, A. L. Y., Zheng, H., Yan, R., & Sung, K. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3483109 · Cited by 36
Ma, T., & Tian, W. (2021) · The Visual Computer · doi:10.1007/s00371-020-01843-3 · Cited by 16
Metadata
- Authors
- Li, Z., Yang, J., Liu, Z., Yang, X., Jeon, G., & Wu, W.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 3862-3871
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963610452
- Cited by
- 883
- References
- 11
- FWCI
- 44.53
- Type
- conference paper
- Language
- en
- Open access
- green
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- Li, Z., Yang, J., Liu, Z., Yang, X., Jeon, G., & Wu, W. (2019). Feedback Network for Image Super-Resolution. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00399
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