caiyuanhao1998/Retinexformer

Health v2 · maintenance only

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1518 stars · 122 forks observed · 2026-08-28

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

Retinexformer is a one-stage Retinex-based Transformer model and toolbox for low-light image enhancement, published at ICCV 2023. It supports over 15 benchmarks, extremely high-resolution enhancement, and low-light object detection applications, and has won or inspired winning solutions in NTIRE challenges from 2024 to 2026.

Use cases

• enhance dark low-light photos
• brighten nighttime images with a transformer model
• preprocess low-light images for object detection
• compare low-light enhancement methods across benchmarks
• enhance very high-resolution images up to 4000x6000
• reproduce ICCV 2023 low-light enhancement research
• build a low-light enhancement solution for NTIRE challenges

When to choose

• you need state-of-the-art low-light image enhancement with pretrained models
• you want a baseline and toolbox covering 15+ benchmarks
• you need illumination-guided transformer modeling for dark images
• you want to enhance images before low-light object detection
• you need a proven competition-winning enhancement method

When to avoid

• you need general-purpose photo editing rather than low-light enhancement
• you want a lightweight CPU-only real-time enhancer
• you need a polished end-user application with a GUI
• your task is unrelated to image restoration or enhancement

Facets

library · maturity active

image-processing computer-vision deep-learning machine-learning computer-vision image-processing deep-learning machine-learning python cross-platform low-light-image-enhancement retinex transformer image-restoration iccv2023 ntire nighttime-enhancement object-detection basicsr research-code gpu linux

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