News

• 28.07.2026: No-reference scripts and improved checkpoints (indicated in filename by _dagger) now available! ArXiv paper also updated with the new values (indicated by $^\dagger$). Both Multinex and Multinex-Nano now clearly outperform prior work!
• 20.06.2026: Clarified repository licensing and commercial-use terms. Multinex is source-available for non-commercial research and educational use under PolyForm Noncommercial 1.0.0; commercial use requires separate written permission.
• 25.04.2026: Model weights for object detection on ExDark are now available for download. Read Detection/README.md for more details.
• 14.04.2026 : Pre-print, code, and paper page for Multinex (accepted at CVPR 2026) are released!

Network Architecture

Introduction

This source-available repository contains the official implementation of Multinex for low-light image enhancement. It provides training and testing code for paired-image enhancement on standard benchmarks, together with pretrained checkpoints for direct evaluation.

Please Note: Installation and running instructions for Object Detection on ExDark are available under Detection/README.md.

Results

Quantitative results for Enhancement (LOL-v1, LOL-v2, MEF, DICM, LIME, NPE), and Detection (ExDark)

LOL Datasets (click to expand)

No-Reference Datasets (click to expand)

Object Detection on ExDark (click to expand)

1. Create Environment

We use a PyTorch 2 environment.

1.1 Create the environment

conda create -n multinex python=3.9 -y
conda activate multinex

1.2 Install PyTorch 2

Example for CUDA 11.8:

# With Pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# With Conda
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia

Change this to accommodate your CUDA version requirements.

1.3 Install dependencies

pip install matplotlib scikit-learn scikit-image opencv-python yacs joblib natsort h5py tqdm tensorboard
pip install einops gdown addict future lmdb numpy pyyaml requests scipy yapf lpips thop timm pytorch_msssim

1.4 Install BasicSR

pip install -e .

Please Note: python setup.py develop has been deprecated. As a result, the above installation is recommended. With this in mind, using basicsr must be done as python -m basicsr.<filename> rather than python basicsr/<filename>.py.

2. Prepare Dataset

Download the LOLv1 and LOLv2 datasets:

LOLv1 - Google Drive

LOLv2 - Google Drive

Organize the datasets as follows:

data/
├── LOLv1/
│   ├── Train/
│   │   ├── input/
│   │   └── target/
│   └── Test/
│       ├── input/
│       └── target/
└── LOLv2/
    ├── Real_captured/
    │   ├── Train/
    │   │   ├── Low/
    │   │   └── Normal/
    │   └── Test/
    │       ├── Low/
    │       └── Normal/
    └── Synthetic/
        ├── Train/
        │   ├── Low/
        │   └── Normal/
        └── Test/
            ├── Low/
            └── Normal/

3. Testing

Model weights are available under pretrained_weights/, and are contained within the repository due to their small file sizes (280 KB for Multinex, 20 KB for Multinex-Nano). These weights are Model Materials covered by the non-commercial license notice in pretrained_weights/README.md and MODEL_CARD.md.

Note: No-reference checkpoints correspond to results reported in the updated ArXiv version, for models indicated with _dagger ($^\dagger$)

Multinex

# LOL-v1
python Enhancement/test.py --opt Options/Multinex_LOL-v1.yaml --weights pretrained_weights/Multinex_LOL-v1.pth --dataset LOL-v1

# LOL-v2-real
python Enhancement/test.py --opt Options/Multinex_LOL-v2-real.yaml --weights pretrained_weights/Multinex_LOL-v2-real.pth --dataset LOL-v2-real

# LOL-v2-synthetic
python Enhancement/test.py --opt Options/Multinex_LOL-v2-synthetic.yaml --weights pretrained_weights/Multinex_LOL-v2-synthetic.pth --dataset LOL-v2-synthetic

# No-reference Datasets
python Enhancement/test_noref.py --opt Options/Multinex_LOL-v2-synthetic.yaml --weights pretrained_weights/Multinex_dagger_NoRef.pth --input_dirs data/DICM data/MEF data/NPE data/LIME --result_dir results/Multinex_NoRef

Multinex-Nano

# LOL-v1
python Enhancement/test.py --opt Options/Multinex-Nano_LOL-v1.yaml --weights pretrained_weights/Multinex-Nano_LOL-v1.pth --dataset LOL-v1

# LOL-v2-real
python Enhancement/test.py --opt Options/Multinex-Nano_LOL-v2-real.yaml --weights pretrained_weights/Multinex-Nano_LOL-v2-real.pth --dataset LOL-v2-real

# LOL-v2-synthetic
python Enhancement/test.py --opt Options/Multinex-Nano_LOL-v2-synthetic.yaml --weights pretrained_weights/Multinex-Nano_LOL-v2-synthetic.pth --dataset LOL-v2-synthetic

# No-reference Datasets
python Enhancement/test_noref.py --opt Options/Multinex-Nano_LOL-v2-synthetic.yaml --weights pretrained_weights/Multinex-Nano_dagger_NoRef.pth --input_dirs data/DICM data/MEF data/NPE data/LIME --result_dir results/Multinex-Nano_NoRef

• Self-ensemble testing strategy

For stronger results, add --self_ensemble argument.

python Enhancement/test.py --opt Options/Multinex_LOL-v1.yaml --weights pretrained_weights/Multinex_LOLv1.pth --dataset LOL_v1 --self_ensemble

4. Training

Training is launched through the BasicSR entrypoint.

# Multinex on LOL-v1
python -m basicsr.train --opt Options/Multinex_LOL-v1.yaml

# Multinex on LOL-v2-real
python -m basicsr.train --opt Options/Multinex_LOL-v2-real.yaml

# Multinex on LOL-v2-synthetic
python -m basicsr.train --opt Options/Multinex_LOL-v2-synthetic.yaml

# Multinex-Nano on LOL-v1
python -m basicsr.train --opt Options/Multinex-Nano_LOL-v1.yaml

# Multinex-Nano on LOL-v2-real
python -m basicsr.train --opt Options/Multinex-Nano_LOL-v2-real.yaml

# Multinex-Nano on LOL-v2-synthetic
python -m basicsr.train --opt Options/Multinex-Nano_LOL-v2-synthetic.yaml

Note: For best results, use val.val_freq: 5 in the yaml configs under Options/ directory. This is especially useful for LOL-v1, where the size of the test set is small, and performance can vary significantly.

5. Citation

Cite our work if Multinex is useful to your research.

@inproceedings{multinex2026,
  title     = {Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex},
  author    = {Alexandru Brateanu and Tingting Mu and Codruta O. Ancuti and Cosmin Ancuti},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2026}
}

License

Copyright (c) 2026 Alexandru Brateanu. Multinex is source-available for non-commercial research and educational use under the PolyForm Noncommercial License 1.0.0. It is not open-source software in the OSI sense, and commercial use is not permitted without a separate written license.

The public non-commercial license applies to original Multinex source code, model definitions, documentation, pretrained weights, checkpoints, configuration files, and related model materials authored by Alexandru Brateanu, unless otherwise stated. Third-party components remain under their original licenses; see THIRD_PARTY_NOTICES.md.

For clarity, "Model Materials" include model weights, checkpoints, adapters, LoRAs, tokenizer files, configuration files, architecture code, training and inference code, documentation, and any modified, merged, quantized, distilled, or fine-tuned versions. See MODEL_CARD.md for the model-specific license notice.

You may use, modify, and share Multinex only for permitted non-commercial purposes, such as:

• academic research
• personal study
• non-commercial experimentation
• reproducibility and benchmarking for non-commercial research
• teaching and educational use

Commercial use includes, but is not limited to:

• use by or for a for-profit company
• integration into a commercial product or service
• internal company research, prototyping, evaluation, or benchmarking
• paid consulting or client work
• SaaS/API deployment
• use in a startup, product pipeline, or revenue-generating system
• training, fine-tuning, or deploying models for commercial advantage
• use of Model Materials in or to support any customer deliverable, commercial R&D, hosted API, internal business process, or revenue-generating workflow

Commercial access is available only through collaboration, partnership, or a separate written commercial license. Commercial evaluation or production use requires a separate written agreement. See COMMERCIAL_USE.md for commercial collaboration options.

For commercial collaboration, contact:

Alexandru Brateanu
Email: [email protected]

This repository is provided as research software. No warranty is provided. See LICENSE for the full license terms and RELEASE_NOTES.md for the license-clarification release note.