ELIEI

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Enhancing Low-light Images Using Infrared Encoded Images

Shulin Tian*,  Yufei Wang*,  Renjie Wan,  Wenhan Yang,  Alex C. Kot,  Bihan Wen   Nanyang Technological University, Hong Kong Baptist University, Peng Cheng Laboratory

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<a href="https://arxiv.org/pdf/2307.04122.pdf">Paper</a> | <a href="https://wyf0912.github.io/ELIEI/ ">Project Page</a> | <a href="https://drive.google.com/drive/folders/1DcBlZz81Sk9Rjh941nSz1D7yxzouXd_4?usp=sharing">Dataset</a>
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</p>

The visibility of low-light images is enhanced by increasing the number of income photons (The right sides of (a) and (b) are amplified by a factor of 3.5 for better visualization).

In this work, we are using a resized version - IR-RGB-resize [Google Drive] for our experiments. The file structure is constructed as follows:

data_root # The paths need to be specified in the training configs under folder `./code/confs/xx.yml`
└── train/
    ├── high/  
    └── low/
└── eval/
    ├── high/
    ├── low/
    └── low-rgb/

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We also relased the original size of images for broadening research purposes IR-RGB [Google Drive], feel free to download and explore!

Results

Quantitative results

The evauluation results on <a href="#Dataset"> IR-RGB dataset </a> are as follows:

Qualitative results

Comparison with other methods

Our method shows better performance in controlling color distortion and detail preservation.

Usage of IR-RGB dataset

(a), (c) are images captured from RGB and IR-RGB space separately under low-light conditions, (d), (f) are respective high-light outputs.

Usage of color alignment loss (CAL)

(c) is the result of our model trained directly w/o adding CAL, and (d) is the output from the same architecture but w/ CAL.

Get Started

Dependencies and Installation

• Python 3.8
• Pytorch 1.9

1. Clone Repo

   git clone https://github.com/shulin16/ELIEI.git

2. Create Conda Environment

   conda create --name ELIEI python=3.8
   conda activate ELIEI

or you can just simply use conda env create -f environment.yml to install all the packages you need.

3. Install Dependencies

   cd ELIEI
   pip install -r requirements.txt

Citation

If you find our work useful for your research, please cite our paper

@inproceedings{tian2023enhancing,
  title={Enhancing Low-Light Images Using Infrared Encoded Images},
  author={Tian, Shulin and Wang, Yufei and Wan, Renjie and Yang, Wenhan and Kot, Alex C and Wen, Bihan},
  booktitle={2023 IEEE International Conference on Image Processing (ICIP)},
  pages={465--469},
  year={2023},
  organization={IEEE}
}

Acknowledgment

We thank <a href="https://wyf0912.github.io/">Yufei Wang</a> for his work <a href="https://wyf0912.github.io/LLFlow/">LLFlow</a>. This work was done at <a href="https://www.ntu.edu.sg/rose">Rapid-Rich Object Search (ROSE) Lab, Nanyang Technological University</a>.