ChaofWang/Awesome-Super-Resolution resource
Collect super-resolution related papers, data, repositories observed · 2026-08-28
Health v2 · maintenance only
76/100
- Activity 98
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2692
- days_rel: n/a
- days_push: 14
- n_releases_24m: 0
Adoption not part of the score
3095 stars · 369 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A curated awesome-list collecting super-resolution papers, datasets, and code repositories, organized by year and approach (deep learning vs. non-DL). It serves as a research reference index rather than executable software.
Use cases
- find super-resolution papers by year
- find image upscaling datasets
- research deep learning image restoration
- find code implementations of super-resolution models
- survey video super-resolution methods
When to choose
- you need a curated index of super-resolution research and code
- you are surveying image restoration literature
- you want datasets for training upscaling models
When to avoid
- you need runnable super-resolution software itself
- you need a maintained library with an API
Facets
learning-resource · maturity active
image-processing machine-learning documentation computer-vision image-processing deep-learning awesome-lists cross-platform awesome-list super-resolution paper-collection research datasets
1 source
- readme: https://github.com/ChaofWang/Awesome-Super-Resolution · fetched 2026-08-28 · 0182416a9aa1
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ChaofWang/Awesome-Super-Resolution | main | 76 |
For agents
markdown · JSON · MCP: product_card(name="ChaofWang/Awesome-Super-Resolution")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem