Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement (ICLR 2023 Oral)

Paper | Project Page

Chongyi Li, Chun-Le Guo, Man Zhou, Zhexin Liang, Shangchen Zhou, Ruicheng Feng, Chen Change Loy

S-Lab, Nanyang Technological University; Nankai University

Updates

• 2023.01.30: This repo is created.

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UHD-LL Dataset

(The datasets are hosted on both Google Drive and BaiduPan)

Dataset	Link	Number	Description
UHD-LL	Google Drive / BaiduPan (key: dz6u)	2,150	A total of 2,000 pairs for training and 150 pairs for testing.

[Unfold] for detailed description of each folder in UHD-LL dataset:

UDH-LL Description training_set/gt normal-light images training_set/input low-light images testing_set/gt normal-light images testing_set/input low-light images

Dependencies and Installation

• Pytorch >= 1.7.1
• CUDA >= 10.1
• Other required packages in requirements.txt

Quick Inference

Before performing the following steps, please download our pretrained model first.

Download Links: [[Google Drive](] [Baidu Disk (password: )]

Then, unzip the file and place the models to ckpts/<dataset_name> directory, separately.

The directory structure will be arranged as:

ckpts
   |- UHD
      |- XX.pt  
   |- LOL
      |- 
   |- LOL2
      |- 

We provide some classic test images in the classic_test_image directory.

Run the following command to process them:

CUDA_VISIBLE_DEVICES=X python src/test_PSNR.py --dataset-name our_test  

The enhanced images will be saved in the results/ directory.

You can also run the following command to process your own images:

CUDA_VISIBLE_DEVICES=X python src/test_PSNR.py \
  --dataset-name our_test -t path/to/your/test/folder   

Train

See python3 src/train.py --h for list of optional arguments, or train.sh for examples.

An example of NH-HAZE dataset.

CUDA_VISIBLE_DEVICES=0,1 python src/train.py \
  --dataset-name NH \
  --train-dir ./data/train_NH/ \
  --valid-dir ./data/valid_NH/ \
  --ckpt-save-path ../ckpts \
  --ckpt-overwrite \
  --nb-epochs 5000 \
  --batch-size 2\
  --train-size 800 1200  \
  --valid-size 800 1200 \
  --loss l1 \
  --plot-stats \
  --cuda   

License

This project is licensed under S-Lab License 1.0. Redistribution and use for non-commercial purposes should follow this license.

Citation

If our work is useful for your research, please consider citing:

@InProceedings{Li2023ICLR,
    author = {Li, Chongyi and Guo, Chun-Le and Zhou, Man and Liang, Zhexin and Zhou, Shangchen and Feng, Ruicheng and Loy, Chen Change},
    title = {Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement},
    booktitle = {ICLR},
    year = {2023}
}

Contact

If you have any questions, please feel free to reach me out at [email protected].