LYT-Net: Lightweight YUV Transformer-based Network for Low-Light Image Enhancement

Ranked #1 on FLOPS(G) (3.49 GFLOPS) and Params(M) (0.045M = 45k Params)

Updates

• 17.07.2024 Released rudimentary PyTorch implementation.
• 03.04.2024 Training code re-added and adjusted.
• 30.01.2024 arXiv pre-print available.
• 10.01.2024 Pre-trained model weights and code for training and testing are released.

Experiment

Please check the TensorFlow and PyTorch folders for library-specific implementations.

Results

Dataset	TensorFlow		PyTorch
	PSNR	SSIM	PSNR	SSIM
LOLv1	27.23	0.853	26.63	0.836
LOLv2-R	27.80	0.873	28.41	0.878
LOLv2-S	29.39	0.939	26.72	0.928

Citation

Preprint Citation

@article{brateanu2024,
  title={LYT-Net: Lightweight YUV Transformer-based Network for Low-Light Image Enhancement},
  author={Brateanu, Alexandru and Balmez, Raul and Avram, Adrian and Orhei, Ciprian},
  journal={arXiv preprint arXiv:2401.15204},
  year={2024}
}