Methods › Computer Vision › Image Data Augmentation › CutBlur
CutBlur
Introduced by Jaejun Yoo et al. in Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
CutBlur is a data augmentation method that is specifically designed for the low-level vision tasks. It cuts a low-resolution patch and pastes it to the corresponding high-resolution image region and vice versa. The key intuition of Cutblur is to enable a model to learn not only "how" but also "where" to super-resolve an image. By doing so, the model can understand "how much" instead of blindly learning to apply super-resolution to every given pixel.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy 1 Apr 2020 · 2 repositories · arXiv:2004.00448Syntology ran 1 of 7 samples · 6 unverified
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Data Augmentation | 1 |
| Denoising | 1 |
| Image Restoration | 1 |
| Image Super-Resolution | 1 |
| Super-Resolution | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections