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Max Image Resolution Enhancer

Max Image Resolution Enhancer is an open-source TensorFlow super-resolution model and Docker-deployable web service that upscales pixelated images by 4× while generating photo-realistic details.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Max Image Resolution Enhancer — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a project needs a deployable super-resolution model for 4× image enlargement, rather than a ready-made image-editing interface.

Who it is for

  • Developers adding image upscaling to a photo-editing application
  • Teams building batch image enhancement for archives
  • Operators seeking a self-hosted image enlargement service

Documented uses

  • Enlarge an RGB image by a factor of four
  • Deploy a model-serving API in a Docker container
  • Choose a documented Kubernetes or OpenShift deployment route
Before you choose

Requirements & limits

The repository is public and identifies Apache 2.0 licensing for the repository and model weights. The README documents a pre-built Docker image and public API, but does not establish current public API availability.

  • Input is specified as an RGB image in HWC format.
  • The repository recommends at least 8 GB memory and 4 CPUs; x86-64/AMD64 requires AVX support.
  • The model was trained on 600,000 OpenImages V4 images.
  • The repository reports evaluation on Set5, Set14 and BSD100 and describes a trade-off favoring visually appealing detail over some PSNR or SSIM results.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start the pre-built model-serving container

Run the Docker image that starts the model-serving API.

Before you start

  • Docker command-line interface
  • Recommended minimum: 8 GB memory and 4 CPUs
  • On x86-64/AMD64, a CPU with AVX support

Steps

  1. Run the command below.

    Documented command
    docker run -it -p 5000:5000 quay.io/codait/max-image-resolution-enhancer

  2. The command pulls the image from Quay if it is not already cached and runs it.

Expected outcome

The Docker image starts the model-serving API on port 5000.

Guide limitations

  • This documents starting the service; it does not provide an image-submission or API-call recipe.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Max Image Resolution EnhancerEnlarge an RGB image by a factor of fourCloud, DockerThe repository is public and identifies Apache 2.0 licensing for the repository and model weights. The README documents a pre-built Docker image and public API, but does not establish current public API availability.
FASHN VTON v1.5Generate a try-on preview from a person image and a garment image.CLI, Linux, macOS, Self-hostedThe repository is public and states an Apache-2.0 license. Local setup requires downloading approximately 2 GB of model weights.
PosteromniRescale a poster to a target aspect ratio using a text prompt.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
IBM/MAX-Image-Resolution-Enhancer · Official homepage title · retained in its source language
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