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BEM

Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement

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/learn @BinCVER/BEM
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0/100

Supported Platforms

Universal

README

Bayesian Enhancement Model

This is the official code for the paper Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement

Demo: BEM's No-Reference Inference with CLIP

clip default

<!-- ![clip noise](./assets/clip_noise.gif) -->

Input Image

<p align="center"> <img src="assets/input_demo.png" align="center" width="100%"> </p>

News 💡

  • 2025.11.08 Our paper has been accepted to AAAI 2026🎉🎉🎉.
  • 2025.03.24 We released the pretrained models for NTIRE 2025 Low-light Image Enhancement Challenge.
  • 2025.03.04 The Code for BEM version 2 has been released.
  • 2024.10.31 Masked Image Modeling (MIM) is implemented to enhance our Stage-II network. We haven’t evaluated its effectiveness, and our team has no future plan to inlcude MIM into the current paper or draw new papers for it. We welcome anyone interested in continuing this research and invite discussions.
  • 2024.10.31 The model checkpoints are released.
  • 2024.10.21 Code has been released. We train each model multiple times and report the median of the test results. Therefore, the results you obtain may be higher than those reported in the paper. If you encounter any difficulties in reproducing our work, please issue them. We look forward to seeing future developments based on the Bayesian Enhancement Model ✨

HD Visulisation

<p align="center"> <img src='assets/vis_hd.png' align="center" > </p>

Bayesian Enhancement Model

<p align="center"> <img src='./assets/twostagev2.png' align="center" > </p>

Checkpoints

We released the pre-trained models Here

Results

We released our enhanced images for all the datasets used in the paper Here

<details close> <summary><b>Performance on LOL-v1, LOL-v2-real and LOL-v2-syn:</b></summary>

results1

</details> <details close> <summary><b>Performance on LIME, NPE, MEF, DICM and VV:</b></summary>

results2

</details> <details close> <summary><b>Performance on UIEB, C60, U50 and UCCS:</b></summary>

results3

</details> <details close> <summary><b>Visulisation on LIME, NPE, MEF, DICM and VV:</b></summary>

results4

</details> <details close> <summary><b>Visulisation on UIEB, U45 and C60:</b></summary>

results5

</details>

Dependencies and Installation

  • Python 3.10.12
  • Pytorch 1.13.1

Create Conda Environment

conda create -n BEM python=3.10.12
conda activate BEM

Clone Repo

git clone https://github.com/BinCVER/Bayesian-Enhancement-Model.git

Install Dependencies

cd Bayesian-Enhancement-Model
pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118

pip install -r requirements.txt

Install BasicSR

python setup.py develop --no_cuda_ext

Install 2D Selective Scan

cd kernels/selective_scan && pip install .

 

Prepare Dataset

Download the LOLv1 and LOLv2 datasets from here.

Download the LIME, NPE, MEF, DICM, and VV datasets from here.

Download UIEB datasets from here.  

Full-Reference Evaluation

Low-Light Image Enhancement

# LOL-v1
python3 Enhancement/eval.py --opt experiments/CG_UNet_LOLv1/CG_UNet_LOLv1.yml --weights experiments/CG_UNet_LOLv1/ckpt.pth \
--cond_opt /experiments/IE_UNet_LOLv1/IE_UNet_LOLv1.yml --cond_weights experiments/IE_UNet_LOLv1/ckpt.pth \
--lpips --dataset LOLv1

# LOL-v2-real
python3 Enhancement/eval.py --opt experiments/CG_UNet_LOLv2Real/CG_UNet_LOLv2Real.yml --weights experiments/CG_UNet_LOLv2Real/ckpt.pth \
--cond_opt /experiments/IE_UNet_LOLv2Real/IE_UNet_LOLv2Real.yml --cond_weights experiments/IE_UNet_LOLv2Real/ckpt.pth \
--lpips --dataset LOLv2Real

# LOL-v2-syn
python3 Enhancement/eval.py --opt experiments/CG_UNet_LOLv2Syn/CG_UNet_LOLv2Syn.yml --weights experiments/CG_UNet_LOLv2Syn/ckpt.pth \
--cond_opt /experiments/IE_UNet_LOLv2Syn/IE_UNet_LOLv2Syn.yml --cond_weights experiments/IE_UNet_LOLv2Syn/ckpt.pth \
--lpips --dataset LOLv2Syn
  • Evaluate using Groundtruth Mean
# LOL-v1
python3 Enhancement/eval.py --opt experiments/CG_UNet_LOLv1/CG_UNet_LOLv1.yml --weights experiments/CG_UNet_LOLv1/ckpt.pth \
--cond_opt /experiments/IE_UNet_LOLv1/IE_UNet_LOLv1.yml --cond_weights experiments/IE_UNet_LOLv1/ckpt.pth \
--lpips --dataset LOLv1 --GT_mean
  • BEM's Deterministic Mode (BEM-DNN)
# LOL-v1
python3 Enhancement/eval.py --opt experiments/CG_UNet_LOLv1/CG_UNet_LOLv1.yml --weights experiments/CG_UNet_LOLv1/ckpt.pth \
--cond_opt /experiments/IE_UNet_LOLv1/IE_UNet_LOLv1.yml --cond_weights experiments/IE_UNet_LOLv1/ckpt.pth \
--lpips --dataset LOLv1 --GT_mean --deterministic

Underwater Image Enhancement

# UIEB
python3 Enhancement/eval.py --opt experiments/CG_UNet_UIEB/CG_UNet_UIEB.yml --weights experiments/CG_UNet_UIEB/ckpt.pth \
--cond_opt /experiments/IE_UNet_UIEB/IE_UNet_UIEB.yml --cond_weights experiments/IE_UNet_UIEB/ckpt.pth \
--lpips -dataset UIEB

No-Reference Evaluation

Low-Light Image Enhancement

# DICM with NIQE
python3 Enhancement/eval.py --opt experiments/CG_UNet_LOLv2Syn/CG_UNet_LOLv2Syn.yml --weights experiments/CG_UNet_LOLv2Syn/ckpt.pth \
--cond_opt /experiments/IE_UNet_LOLv2Syn/IE_UNet_LOLv2Syn.yml --cond_weights experiments/IE_UNet_LOLv2Syn/ckpt.pth \
--dataset DICM --input_dir datasets/DICM --no_ref  niqe

# VV with CLIP-IQA
python3 Enhancement/eval.py --opt experiments/CG_UNet_LOLv2Syn/CG_UNet_LOLv2Syn.yml --weights experiments/CG_UNet_LOLv2Syn/ckpt.pth \
--cond_opt /experiments/IE_UNet_LOLv2Syn/IE_UNet_LOLv2Syn.yml --cond_weights experiments/IE_UNet_LOLv2Syn/ckpt.pth \
--dataset DICM --input_dir datasets/VV --no_ref  clip

Underwater Image Enhancement


# C60 with UIQM
python3 Enhancement/eval.py --opt experiments/CG_UNet_UIEB/CG_UNet_UIEB.yml --weights experiments/CG_UNet_UIEB/ckpt.pth \
--cond_opt /experiments/IE_UNet_UIEB/IE_UNet_UIEB.yml --cond_weights experiments/IE_UNet_UIEB/ckpt.pth \
--dataset DICM --input_dir datasets/C60 --no_ref uiqm_uciqe --uiqm_weight 1.0

# C60 with UCIQE
python3 Enhancement/eval.py --opt experiments/CG_UNet_UIEB/CG_UNet_UIEB.yml --weights experiments/CG_UNet_UIEB/ckpt.pth \
--cond_opt /experiments/IE_UNet_UIEB/IE_UNet_UIEB.yml --cond_weights experiments/IE_UNet_UIEB/ckpt.pth \
--dataset DICM --input_dir datasets/C60 --no_ref uiqm_uciqe --uiqm_weight 0.0

# U45 with UIQM
python3 Enhancement/eval.py --opt experiments/CG_UNet_UIEB/CG_UNet_UIEB.yml --weights experiments/CG_UNet_UIEB/ckpt.pth \
--cond_opt /experiments/IE_UNet_UIEB/IE_UNet_UIEB.yml --cond_weights experiments/IE_UNet_UIEB/ckpt.pth \
--dataset DICM --input_dir datasets/U45 --no_ref uiqm_uciqe --uiqm_weight 1.0

# U45 with UCIQE
python3 Enhancement/eval.py --opt experiments/CG_UNet_UIEB/CG_UNet_UIEB.yml --weights experiments/CG_UNet_UIEB/ckpt.pth \
--cond_opt /experiments/IE_UNet_UIEB/IE_UNet_UIEB.yml --cond_weights experiments/IE_UNet_UIEB/ckpt.pth \
--dataset DICM --input_dir datasets/U45 --no_ref uiqm_uciqe --uiqm_weight 0.0

# UCCS with UIQM
python3 Enhancement/eval.py --opt experiments/CG_UNet_UIEB/CG_UNet_UIEB.yml --weights experiments/CG_UNet_UIEB/ckpt.pth \
--cond_opt /experiments/IE_UNet_UIEB/IE_UNet_UIEB.yml --cond_weights experiments/IE_UNet_UIEB/ckpt.pth \
--dataset DICM --input_dir datasets/U45 --no_ref uiqm_uciqe --uiqm_weight 1.0

# UCCS with UCIQE
python3 Enhancement/eval.py --opt experiments/CG_UNet_UIEB/CG_UNet_UIEB.yml --weights experiments/CG_UNet_UIEB/ckpt.pth \
--cond_opt /experiments/IE_UNet_UIEB/IE_UNet_UIEB.yml --cond_weights experiments/IE_UNet_UIEB/ckpt.pth \
--dataset DICM --input_dir datasets/UCCS --no_ref uiqm_uciqe --uiqm_weight 0.0

Training


# Stage-I on LOL-v1
python3 basicsr/train.py --opt Options/CG_UNet_LOLv1.yml
# Stage-II on LOL-v1
python3 basicsr/train.py --opt Options/IE_UNet_LOLv1.yml


# Stage-I on LOL-v2-real
python3 basicsr/train.py --opt Options/CG_UNet_LOLv2Real.yml
# Stage-II on LOL-v2-real
python3 basicsr/train.py --opt Options/IE_UNet_LOLv2Real.yml


# Stage-I on LOL-v2-syn
python3 basicsr/train.py --opt Options/CG_UNet_LOLv2Syn.yml
# Stage-II on LOL-v2-syn
python3 basicsr/train.py --opt Options/IE_UNet_LOLv2Syn.yml


# Stage-I on UIEB
python3 basicsr/train.py --opt Options/CG_UNet_UIEB.yml
# Stage-II on UIEB
python3 basicsr/train.py --opt Options/IE_UNet_UIEB.yml

 

Citation

@article{huang2026bayesian,
  title={Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement},
  author={Huang, Guoxi and Yang, Qirui and Qi, Zipeng and Lin, RuiRui and Bull, David and Anantrasirichai, Nantheera},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2026}
}

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Audited on Feb 2, 2026

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