cchen156/Learning-to-See-in-the-Dark

Health v2 · maintenance only

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5565 stars · 837 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

TensorFlow implementation of 'Learning to See in the Dark' (CVPR 2018), a deep learning model that brightens very dark, short-exposure RAW photos. It includes training/testing code, pretrained models, and the See-in-the-Dark Sony/Fuji datasets.

Use cases

• enhance dark low-light photos with deep learning
• train a low-light RAW image enhancement model
• denoise and brighten short-exposure RAW images
• reproduce CVPR 2018 See-in-the-Dark results
• download the SID Sony and Fuji benchmark datasets
• run inference on near-black RAW photos

When to choose

• you need research-grade low-light RAW image enhancement
• you want to benchmark against the SID dataset
• you're working with TensorFlow and RAW files via rawpy

When to avoid

• you need a maintained production pipeline (Python 2.7 / TF 1.x era code)
• you want a simple photo app without GPU setup
• you can't handle large 25-52 GB dataset downloads

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision computer-vision image-processing deep-learning machine-learning python low-light-image-enhancement raw-image-processing tensorflow cvpr-2018 computational-photography research-code see-in-the-dark-dataset linux gpu

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