krasserm/super-resolution

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1511 stars · 342 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A TensorFlow 2.x implementation of EDSR, WDSR, and SRGAN models for single image super-resolution, with a high-level training API and DIV2K dataset support. It includes pre-trained weights and example notebooks for training, fine-tuning, and upscaling images.

Use cases

• upscale low-resolution images with deep learning
• train an EDSR or WDSR super-resolution model
• fine-tune a super-resolution model with SRGAN for photo-realistic results
• reproduce NTIRE challenge super-resolution papers
• increase image resolution 2x, 3x, 4x, or 8x in Python

When to choose

• you want a TensorFlow 2.x/Keras codebase for single-image super-resolution
• you need to train or fine-tune EDSR, WDSR, or SRGAN on DIV2K or custom data
• you want ready-to-use pre-trained weights for quick upscaling experiments

When to avoid

• you need a production-ready image upscaling service or GUI rather than a research library
• you work outside the TensorFlow/Keras ecosystem (e.g. prefer PyTorch)
• you need actively maintained code with recent updates

Facets

library · maturity maintenance

machine-learning image-processing deep-learning computer-vision image-processing deep-learning machine-learning python super-resolution tensorflow2 keras edsr wdsr srgan div2k image-upscaling gpu

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