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License Super Resolution

License Super Resolution is a self-hosted TensorFlow 2 project for preprocessing license-plate image data and training a GAN-based single-image super-resolution model that reconstructs plate images.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

License Super Resolution — 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 you want a code-based starting point for experimenting with super-resolution on license-plate images and can manage the data preparation and listed Python dependencies.

Who it is for

  • Developers and researchers prototyping license-plate image enhancement workflows.
  • TensorFlow users exploring adversarial single-image super-resolution.

Documented uses

  • Preprocess raw license-plate image datasets before training.
  • Train a generator-discriminator model for plate-image reconstruction.
  • Evaluate reconstructed images using the project's training and evaluation workflow.
Before you choose

Requirements & limits

The repository is public and lists an MIT license. The project is self-hosted.

  • Preprocessing requires Dask 2.11.0 or later and PIL 6.2.2 or later.
  • Training and evaluation list TensorFlow 2.1.0 or later, NumPy 1.18.1 or later, and Matplotlib 3.1.3 or later.
  • The README identifies CCPD as the dataset used.
  • The repository documents preprocessing and model architecture but does not give script arguments in the README.

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

Prepare license-plate data for the model workflow

Preprocess a raw plate-image dataset as the documented prerequisite to model training.

Before you start

  • A raw image dataset; the project identifies CCPD as its dataset.
  • Dask 2.11.0 or later and PIL 6.2.2 or later for preprocessing.

Steps

  1. Install the listed preprocessing dependencies.

  2. Run the project's preprocess.py script on the raw dataset before training.

Expected outcome

The raw dataset has gone through the project's documented preprocessing stage and is ready for the subsequent training workflow.

Guide limitations

  • The README does not specify command-line arguments or the exact output format for preprocess.py.
  • This guide covers preprocessing only, not model training or inference.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
zzxvictor/License-super-resolution · Official homepage title · retained in its source language
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