# IBM/MAX-Image-Resolution-Enhancer

Upscale an image by a factor of 4, while generating photo-realistic details.

Repository: https://github.com/IBM/MAX-Image-Resolution-Enhancer
Canonical: https://ross.abutalabs.com/products/max-image-resolution-enhancer
Homepage: https://developer.ibm.com/exchanges/models/all/max-image-resolution-enhancer/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: computer-vision, machine-learning, ai, neural-network, ibm, docker-image, codait, machine-learning-models, tensorflow
Last push: 2025-09-17T20:30:28+00:00

## Health v2 (maintenance only)
Score: 42/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 42, release rhythm 8, longevity 100
- inputs: {"age_days": 2724, "days_push": 350, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1040, forks 161 (observed 2026-08-28T04:03:20.113731+00:00)

## What it is
An IBM Model Asset Exchange project that deploys an SRGAN-based image super-resolution model as a web service in a Docker container. It upscales pixelated images by a factor of 4 while generating photo-realistic details.

## Use cases
- upscale a low-resolution image by 4x
- enhance pixelated photos with realistic details
- run super-resolution as a REST API
- deploy an image enhancement model in Docker
- restore detail in small images for web or print
- compare SRGAN implementations on PSNR and SSIM benchmarks

## When to choose
- you need a ready-to-deploy super-resolution web service with a public API
- you want a Docker-packaged SRGAN model trained on OpenImages V4
- you prefer photo-realistic output over strictly highest PSNR scores

## When to avoid
- you need real-time or very high-throughput upscaling
- you want a lightweight client-side or CLI-only tool without a server
- you need a model actively developed with recent research improvements

## Facets
- artifact type: service
- maturity: maintenance
- function: image-processing, machine-learning, http-server, llm-inference
- domain: computer-vision, image-processing, machine-learning, deep-learning
- platform: python, self-hosted
- tags: super-resolution, srgan, gan, tensorflow, image-upscaling, model-asset-exchange, rest-api, docker, web-server

## Member repositories
- IBM/MAX-Image-Resolution-Enhancer (main) score 42

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:20.113731+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 2026-08-30T07:03:07.297138+00:00, confidence not recorded.
  - readme: https://github.com/IBM/MAX-Image-Resolution-Enhancer (fetched 2026-08-28T04:03:20.113731+00:00, sha 301bc7c25ff2)
- Data as of 2026-08-30T08:39:29.467469+00:00.