Research map / Image generation / Super-resolution
Camera Lens Super-Resolution
Check 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 with realistic R-V degradation instead of bicubic downsampling | VDSR · City100 test, average | PSNR: 28.97 -> 30.26 | Helped |
| Train with realistic R-V degradation instead of Gaussian downsampling | VDSR · City100 test, average | PSNR: 28.92 -> 30.26 | Helped |
| Train with realistic R-V degradation instead of bicubic downsampling | SRGAN · City100 test, average | Ma's metric: 4.77 -> 6.69 | Helped |
| Train with realistic R-V degradation instead of bicubic downsampling | SRGAN · City100 test, average | VGG metric: 0.7071 -> 0.5124 | Helped |
Closest papers by meaning
- Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real World (CVPR 2024 · similarity 0.88)
- Sequence Matters: Harnessing Video Models in 3D Super-Resolution (AAAI 2025 · similarity 0.87)
- KeDuSR: Real-World Dual-Lens Super-Resolution via Kernel-Free Matching (AAAI 2024 · similarity 0.87)
- Towards Realistic Data Generation for Real-World Super-Resolution (ICLR 2025 · similarity 0.87)
- SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution (ICLR 2024 · similarity 0.86)
- Unsupervised Diffusion-Based Degradation Modeling for Real-World Super-Resolution (AAAI 2025 · similarity 0.86)
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.