alexjc/neural-enhance
Super Resolution for images using deep learning. observed · 2026-08-28
Health v2 · maintenance only
10/100
- Activity 0
- Release rhythm 8
- Longevity 100
Flags: archived
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: 3623
- days_rel: n/a
- days_push: 2073
- n_releases_24m: 0
Adoption not part of the score
11865 stars · 1355 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Neural Enhance is a Python command-line tool that upscales images 2x or 4x using deep learning super-resolution models. It ships with pre-trained models and can run on CPU or CUDA-enabled GPUs.
Use cases
- upscale low resolution photos with deep learning
- repair jpeg artifacts in images
- zoom images 2x or 4x using a neural network
- enhance old photos to higher resolution
- run super resolution on gpu with cuda
When to choose
- you want a ready-to-run CLI for image super-resolution with pre-trained models
- you have CUDA hardware and want fast GPU upscaling
- you need to repair compression artifacts while upscaling photos
When to avoid
- you need actively maintained software or recent framework support
- you want a GUI or web-based upscaling tool
- you need state-of-the-art super-resolution results on modern benchmarks
Facets
cli-tool · maturity abandoned
image-processing deep-learning machine-learning image-processing deep-learning machine-learning python cli windows super-resolution neural-networks image-upscaling photo-enhancement cuda linux macos
1 source
- readme: https://github.com/alexjc/neural-enhance · fetched 2026-08-28 · e26845d196f5
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| alexjc/neural-enhance | main | 10 |
For agents
markdown · JSON · MCP: product_card(name="alexjc/neural-enhance")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem