https://github.com/LowLevelAI/GLARE

Official implementation of GLARE, which is accpeted by ECCV 2024.
https://github.com/LowLevelAI/GLARE

codebook eccv24 low-light-image-enhancement normalizing-flow

Last synced: 3 months ago
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Official implementation of GLARE, which is accpeted by ECCV 2024.

Host: GitHub
URL: https://github.com/LowLevelAI/GLARE
Owner: LowLevelAI
License: apache-2.0
Created: 2024-07-08T04:55:13.000Z (11 months ago)
Default Branch: main
Last Pushed: 2024-12-31T11:25:01.000Z (5 months ago)
Last Synced: 2024-12-31T12:25:52.838Z (5 months ago)
Topics: codebook, eccv24, low-light-image-enhancement, normalizing-flow
Language: Python
Homepage: https://arxiv.org/pdf/2407.12431
Size: 3.4 MB
Stars: 83
Watchers: 5
Forks: 3
Open Issues: 9
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt

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README

# [ECCV 2024] GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval [[Paper]](https://arxiv.org/pdf/2407.12431)

Han Zhou^1,, Wei Dong^1,, Xiaohong Liu^2,†, Shuaicheng Liu³, Xiongkuo Min², Guangtao Zhai², Jun Chen^1,†

¹McMaster University, ²Shanghai Jiao Tong University,

³University of Electronic Science and Technology of China

^Equal Contribution, ^†Corresponding Authors

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/glare-low-light-image-enhancement-via/low-light-image-enhancement-on-lolv2)](https://paperswithcode.com/sota/low-light-image-enhancement-on-lolv2?p=glare-low-light-image-enhancement-via)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/glare-low-light-image-enhancement-via/low-light-image-enhancement-on-lolv2-1)](https://paperswithcode.com/sota/low-light-image-enhancement-on-lolv2-1?p=glare-low-light-image-enhancement-via)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/glare-low-light-image-enhancement-via/low-light-image-enhancement-on-lol)](https://paperswithcode.com/sota/low-light-image-enhancement-on-lol?p=glare-low-light-image-enhancement-via)

### Introduction
This repository represents the official implementation of our ECCV 2024 paper titled GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval. If you find this repo useful, please give it a star ⭐ and consider citing our paper in your research. Thank you.

[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)

We present GLARE, a novel network for low-light image enhancement.

- Codebook-based LLIE: exploit normal-light (NL) images to extract NL codebook prior as the guidance.
- Generative Feature Learning: develop an invertible latent normalizing flow strategy for feature alignment.
- Adaptive Feature Transformation: adaptively introduces input information into the decoding process and allows flexible adjustments for users.
- Future: network structure can be meticulously optimized to improve efficiency and performance in the future.

### Overall Framework
![teaser](images/framework.png)

## 📢 News
2024-12-31: Train code has been released. ⭐

2024-09-25: Our another paper ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction has been accepted by NeurIPS 2024. Code and pre-print will be released at: . :rocket:

2024-09-21: Inference code for unpaired images and pre-trained models for LOL-v2-real is released! :rocket:

2024-07-21: Inference code and pre-trained models for LOL is released! Feel free to use them. ⭐

2024-07-21: [License](LICENSE.txt) is updated to Apache License, Version 2.0. 💫

2024-07-19: Paper is available at: . :tada:

2024-07-01: Our paper has been accepted by ECCV 2024. Code and Models will be released. :rocket:

## 🛠️ Setup

The inference code was tested on:

- Ubuntu 22.04 LTS, Python 3.8, CUDA 11.3, GeForce RTX 2080Ti or higher GPU RAM.

### 📦 Repository

Clone the repository (requires git):

```bash
git clone https://github.com/LowLevelAI/GLARE.git
cd GLARE
```

### 💻 Dependencies

- Make Conda Environment: Using [Conda](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) to create the environment:

```bash
conda create -n glare python=3.8
conda activate glare
```
- Then install dependencies:

```bash
conda install pytorch=1.11 torchvision cudatoolkit=11.3 -c pytorch
pip install addict future lmdb numpy opencv-python Pillow pyyaml requests scikit-image scipy tqdm yapf einops tb-nightly natsort
pip install pyiqa==0.1.4
pip install pytorch_lightning==1.6.0
pip install --force-reinstall charset-normalizer==3.1.0
```

- Build CUDA extensions:

```bash
cd GLARE/defor_cuda_ext
BASICSR_EXT=True python setup.py develop
```

- Move CUDA extensions (/GLARE/defor_cuda_ext/basicsr/ops/dcn/deform_conv_ext.xxxxxx.so) to the path: /GLARE/code/models/modules/ops/dcn/.

## 🏃 Testing on benchmark datasets

### 📷 Download following datasets:

LOL [Google Drive](https://drive.google.com/file/d/1L-kqSQyrmMueBh_ziWoPFhfsAh50h20H/view?usp=sharing)

LOL-v2 [Google Drive](https://drive.google.com/file/d/1Ou9EljYZW8o5dbDCf9R34FS8Pd8kEp2U/view?usp=sharing)

### ⬇ Download pre-trained models

Download [pre-trained weights for LOL](https://drive.google.com/drive/folders/1DuATvqpNgRGlPq5_LvvzghkFdFL9sYvq), [pre-trained weights for LOL-v2-real](https://drive.google.com/drive/folders/1Cesn3jJAdxjT7DDZCTMU8Vt2CnauBL7F?usp=drive_link) and place them to folder `pretrained_weights_lol`, `pretrained_weights_lol-v2-real`, respectively.

### 🚀 Run inference

For LOL dataset

```bash
python code/infer_dataset_lol.py
```

For LOL-v2-real dataset

```bash
python code/infer_dataset_lolv2-real.py
```

For unpaired testing, please make sure the dataroot_unpaired* in the .yml file is correct.

```bash
python code/infer_unpaired.py
```

You can find all results in `results/`. Enjoy!

## 🏋️ Training

Download [VQGAN weight for LOL](https://drive.google.com/drive/folders/1DuATvqpNgRGlPq5_LvvzghkFdFL9sYvq),and place it to folder `pretrained_weights_lol`.

For stage2 training:
```bash
python code/train_stage2.py
```

For stage2 testing:
```bash
python code/test_stage2.py
```

For stage3 training:
```bash
python code/train_stage3.py
```

For stage3 testing:
```bash
python code/test_stage3.py
```

## ✏️ Contributing

Please refer to [this](CONTRIBUTING.md) instruction.

## 🎓 Citation

Please cite our paper:

```bibtex
@inproceedings{han2025glare,
title={Glare: Low light image enhancement via generative latent feature based codebook retrieval},
author={Zhou, Han and Dong, Wei and Liu, Xiaohong and Liu, Shuaicheng and Min, Xiongkuo and Zhai, Guangtao and Chen, Jun},
booktitle={European Conference on Computer Vision},
pages={36--54},
year={2025},
organization={Springer}
}

@article{GLARE,
title = {GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval},
author = {Zhou, Han and Dong, Wei and Liu, Xiaohong and Liu, Shuaicheng and Min, Xiongkuo and Zhai, Guangtao and Chen, Jun},
journal = {arXiv preprint arXiv:2407.12431},
year = {2024}
}
```

## 🎫 License

This work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)).

By downloading and using the code and model you agree to the terms in the [LICENSE](LICENSE.txt).

[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)