Research map / Image generation / Super-resolution
“Zero-Shot” Super-Resolution Using Deep Internal Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train an image-specific CNN on the test image’s internal downscaling relationships | Zero-Shot Super-Resolution · Real old photos, noisy images, and biological data | super-resolution quality | Helped |
| Train an image-specific CNN on the test image’s internal downscaling relationships | Zero-Shot Super-Resolution · High-quality images with a known constant blur-kernel | super-resolution quality | Mixed |
Closest papers by meaning
- Meta-Transfer Learning for Zero-Shot Super-Resolution (CVPR 2020 · similarity 0.91)
- Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (CVPR 2016 · similarity 0.90)
- Noise-Free Optimization in Early Training Steps for Image Super-resolution (AAAI 2024 · similarity 0.89)
- Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning (CVPR 2024 · similarity 0.89)
- Learning Controllable Degradation for Real-World Super-Resolution via Constrained Flows (ICML 2023 · similarity 0.89)
- Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution (CVPR 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.