Accepted in ACCV'24, Hanoi, Veitnam

LoLI-Street: Benchmarking Low-Light Image Enhancement and Beyond (Download Paper)

Md Tanvir Islam ¹, Inzamamul Alam ¹, Simon S. Woo ^{1, *}, Saeed Anwar ², IK Hyun Lee ³, Khan Muhammad ^{1, *}

| 1. Sungkyunkwan University, South Korea | 2. The Australian National University, Australia | 3. Tech University of Korea, South Korea |

| *Corresponding Author |

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Dataset Download

1. Kaggle: https://www.kaggle.com/datasets/tanvirnwu/loli-street-low-light-image-enhancement-of-street
2. Google Drive: https://drive.google.com/file/d/1xfATFqrYvMU5a4eLJ5iMi7PVts1x3mmi/view?usp=sharing

Code is coming soon.

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LoLI-Street Dataset

Proposed: TriFuse

Dependencies

pip install -r requirements.txt

Cite this Paper

If you find our work useful in your research, please consider citing our paper:

@misc{islam2024lolistreetbenchmarkinglowlightimage,
      title={LoLI-Street: Benchmarking Low-Light Image Enhancement and Beyond}, 
      author={Md Tanvir Islam and Inzamamul Alam and Simon S. Woo and Saeed Anwar and IK Hyun Lee and Khan Muhammad},
      year={2024},
      eprint={2410.09831},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2410.09831}, 
}