The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks
Open accessAbstract
We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable. Different from traditional super-resolution formulation, the low-resolution input is further degraded by noises and blurring. This complicated setting makes supervised learning and accurate kernel estimation impossible. To solve this problem, we resort to unsupervised learning without paired data, inspired by the recent successful image-to-image translation applications. With generative adversarial networks (GAN) as the basic component, we propose a Cycle-in-Cycle network structure to tackle the problem within three steps. First, the noisy and blurry input is mapped to a noise-free low-resolution space. Then the intermediate image is up-sampled with a pre-trained deep model. Finally, we fine-tune the two modules in an end-to-end manner to get the high-resolution output. Experiments on NTIRE2018 datasets demonstrate that the proposed unsupervised method achieves comparable results as the state-of-the-art supervised models.
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Cites
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GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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Self-citationAccurate Image Super-Resolution Using Very Deep Convolutional Networks
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Cited by
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231
Deep Learning on Image Denoising: An overview
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Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
Zhang, K., Liang, J., Van Gool, L., & Timofte, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00475 · Cited by 760
Blind Super-Resolution With Iterative Kernel Correction
Gu, J., Lu, H., Zuo, W., & Dong, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00170 · Cited by 580
Self-citationYou, C., Cong, W., Vannier, M. W., Saha, P. K., Hoffman, E. A., Wang, G., Li, G., Zhang, Y., Zhang, X., Shan, H., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2922960 · Cited by 459
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Metadata
- Authors
- Yuan, Y., Liu, S., Zhang, J., Zhang, Y., Dong, C., & Lin, L.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 814-81409
- OpenAlex
- W2962903125
- Cited by
- 594
- References
- 36
- FWCI
- 17.00
- Type
- conference paper
- Language
- en
- Open access
- green
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- Yuan, Y., Liu, S., Zhang, J., Zhang, Y., Dong, C., & Lin, L. (2018). Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw.2018.00113
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