NVlabs/noise2noise

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1563 stars · 330 forks observed · 2026-08-28

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

Official TensorFlow implementation of the Noise2Noise ICML 2018 paper, which trains image restoration networks using only corrupted (noisy) images without clean ground truth. It supports photographic denoising, Monte Carlo render denoising, and undersampled MRI reconstruction.

Use cases

• denoise photos without clean training data
• remove noise from images using deep learning
• train an image restoration model from noisy pairs only
• denoise synthetic Monte Carlo rendered images
• reconstruct undersampled MRI scans
• reproduce the Noise2Noise paper results

When to choose

• you want to reproduce or build on the Noise2Noise paper
• you have pairs of independently corrupted images but no clean ground truth
• you need a research baseline for blind image denoising

When to avoid

• you need a production-ready, actively maintained denoising library
• you use modern frameworks like PyTorch
• you need a commercial license (code is CC BY-NC 4.0)
• you need up-to-date TensorFlow compatibility

Facets

library · maturity maintenance

machine-learning image-processing deep-learning machine-learning computer-vision image-processing python denoising tensorflow research-code image-restoration mri icml-2018 gpu linux

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markdown · JSON · MCP: product_card(name="NVlabs/noise2noise")