The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Zero-Shot Super-Resolution Using Deep Internal Learning
Open accessAbstract
Deep Learning has led to a dramatic leap in SuperResolution (SR) performance in the past few years. However, being supervised, these SR methods are restricted to specific training data, where the acquisition of the low-resolution (LR) images from their high-resolution (HR) counterparts is predetermined (e.g., bicubic downscaling), without any distracting artifacts (e.g., sensor noise, image compression, non-ideal PSF, etc). Real LR images, however, rarely obey these restrictions, resulting in poor SR results by SotA (State of the Art) methods. In this paper we introduce "Zero-Shot" SR, which exploits the power of Deep Learning, but does not rely on prior training. We exploit the internal recurrence of information inside a single image, and train a small image-specific CNN at test time, on examples extracted solely from the input image itself. As such, it can adapt itself to different settings per image. This allows to perform SR of real old photos, noisy images, biological data, and other images where the acquisition process is unknown or non-ideal. On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods. To the best of our knowledge, this is the first unsupervised CNN-based SR method.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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
Super-resolution from a single image
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Self-citationZero-Shot Learning — The Good, the Bad and the Ugly
Xian, Y., Schiele, B., & Akata, Z. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.328 · Cited by 891
Internal statistics of a single natural image
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Self-citationAccurate Blur Models vs. Image Priors in Single Image Super-resolution
Efrat, N., Glasner, D., Apartsin, A., Nadler, B., & Levin, A. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.352 · Cited by 200
Cited by
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
Unsupervised Degradation Representation Learning for Blind Super-Resolution
Wang, L., Wang, Y., Dong, X., Xu, Q., Yang, J., An, W., & Guo, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01044 · Cited by 412
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A. A., & Hardt, M. (2019) · arXiv · doi:10.48550/arxiv.1909.13231 · Cited by 393
Generative Diffusion Prior for Unified Image Restoration and Enhancement
Fei, B., Lyu, Z., Pan, L., Zhang, J., Yang, W., Luo, T., Zhang, B., & Dai, B. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00958 · Cited by 279
Metadata
- Authors
- Shocher, A., Cohen, N., & Irani, M.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 3118-3126
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2779113761
- Cited by
- 936
- References
- 6
- FWCI
- 30.00
- Type
- conference paper
- Language
- en
- Open access
- green
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