tencent/real-sr — explained in plain English
Analysis updated 2026-07-17 · repo last pushed 2022-01-16
Sharpen blurry or noisy phone photos using a pre-trained model without any coding.
Restore compressed JPEG images with visible processing artifacts.
Train a custom super-resolution model on your own dataset of real-world degraded images.
Batch-process a folder of low-quality images into enhanced versions.
| tencent/real-sr | gvclab/cutclaw | jd-opensource/joyai-echo | |
|---|---|---|---|
| Stars | 824 | 831 | 758 |
| Language | Python | Python | Python |
| Last pushed | 2022-01-16 | — | — |
| Maintenance | Dormant | — | — |
| Setup difficulty | moderate | moderate | hard |
| Complexity | 4/5 | 3/5 | 5/5 |
| Audience | researcher | general | researcher |
Figures from each repo's GitHub metadata at analysis time.
GPU recommended for efficient inference, standalone executables are available for non-coders.
RealSR sharpens blurry, noisy real-world photos by learning how actual camera degradation happens, unlike tools trained only on artificially blurred images, it won CVPR NTIRE 2020's top prize.
Mainly Python. The stack also includes Python, PyTorch.
Dormant — no commits in 2+ years (last push 2022-01-16).
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
double-check against the repo, no cap.