atriumlts/subpixel

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2123 stars · 299 forks observed · 2026-08-28

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

A TensorFlow reimplementation of the efficient sub-pixel convolutional neural network (ESPCN) for single-image super-resolution, based on Shi et al.'s CVPR 2016 paper. It includes training notebooks and example 4x upscaled images.

Use cases

• upscale low-resolution images 4x with a neural network
• implement sub-pixel convolution layers in TensorFlow
• reproduce the ESPCN super-resolution paper
• train a super-resolution model on custom images
• compare transposed convolutions vs sub-pixel convolutions
• learn how super-resolution CNNs work

When to choose

• you need a reference implementation of sub-pixel convolution in TensorFlow
• you want to experiment with or study ESPCN-style super-resolution
• you need a lightweight starting point for image upscaling research

When to avoid

• you need a production-ready or actively maintained super-resolution toolkit
• you use PyTorch or other frameworks instead of TensorFlow
• you need state-of-the-art super-resolution quality rather than a classic baseline

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision machine-learning deep-learning image-processing computer-vision python super-resolution subpixel-convolution tensorflow research-code jupyter-notebook

Member repositories

For agents

markdown · JSON · MCP: product_card(name="atriumlts/subpixel")