# alexjc/neural-enhance

Super Resolution for images using deep learning.

Repository: https://github.com/alexjc/neural-enhance
Canonical: https://ross.abutalabs.com/products/neural-enhance
Language: Python
License: AGPL-3.0
License Family: copyleft
Archived: true
Last push: 2020-12-29T08:43:04+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3623, "days_push": 2073, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 11865, forks 1355 (observed 2026-08-28T04:10:50.338914+00:00)

## What it is
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
- artifact type: cli-tool
- maturity: abandoned
- function: image-processing, deep-learning, machine-learning
- domain: image-processing, deep-learning, machine-learning
- platform: python, cli, windows
- tags: super-resolution, neural-networks, image-upscaling, photo-enhancement, cuda, linux, macos

## Member repositories
- alexjc/neural-enhance (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:50.338914+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:15:15.216125+00:00, confidence not recorded.
  - readme: https://github.com/alexjc/neural-enhance (fetched 2026-08-28T04:10:50.338914+00:00, sha e26845d196f5)
- Data as of 2026-08-30T08:39:29.467469+00:00.