[SPL 2025] LYT-Net: Lightweight YUV Transformer-based Network for Low-Light Image Enhancement

Abstract

This letter introduces LYT-Net, a novel lightweight transformer-based model for low-light image enhancement (LLIE). LYT-Net consists of several layers and detachable blocks, including our novel blocks--Channel-Wise Denoiser (CWD) and Multi-Stage Squeeze & Excite Fusion (MSEF)--along with the traditional Transformer block, Multi-Headed Self-Attention (MHSA). In our method we adopt a dual-path approach, treating chrominance channels U and V and luminance channel Y as separate entities to help the model better handle illumination adjustment and corruption restoration. Our comprehensive evaluation on established LLIE datasets demonstrates that, despite its low complexity, our model outperforms recent LLIE methods. The source code and pre-trained models are available at this https URL.

🆕 Updates

• 14.04.2026 📢🥳 Check out our new CVPR 2026 paper: Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex: 📄Project Page, 📎Paper, ⭐Code.
• 28.07.2025 ✨ Check out our new multimodal framework: ModalFormer: Multimodal Transformer for Low-Light Image Enhancement! Paper and HF Demo coming soon!
• 27.07.2025 🤗 LYT-Net now has a new HuggingFace page! Check it out here! HF Demo coming soon!
• 09.05.2025 📢 Check out our other works on Low-light Image Enhancement and Image Denoising!
• 21.04.2025 📝 LYT-Net is published as a IEEE Signal Processing Letters paper. Link to paper.
• 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

@article{brateanu2025lyt,
  author={Brateanu, Alexandru and Balmez, Raul and Avram, Adrian and Orhei, Ciprian and Ancuti, Cosmin},
  journal={IEEE Signal Processing Letters}, 
  title={LYT-NET: Lightweight YUV Transformer-based Network for Low-light Image Enhancement}, 
  year={2025},
  volume={},
  number={},
  pages={1-5},
  doi={10.1109/LSP.2025.3563125}}


@article{brateanu2024lyt,
  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 and Cosmin, Ancuti},
  journal={arXiv preprint arXiv:2401.15204},
  year={2024}
}