UDnet: underwater image enhancement

Pretrained weights for UDnet, from Adaptive deep learning framework for robust unsupervised underwater image enhancement (Saleh et al., Expert Systems with Applications, 2025).

• Code: https://github.com/alzayats/UDnet
• Paper: https://www.sciencedirect.com/science/article/pii/S0957417424031816
• Gallery: https://alzayats.github.io/UDnet/

UDnet corrects colour casts and improves contrast in underwater photos and keeps the input resolution.

Model	Status	File	Notes
UDnet V1	Released	UDnet.pth (5.6 MB)	The paper model. Fully unsupervised: trained without any reference images. 1.4 M parameters, runs on a CPU.
UDnet V2	Preview	UDnet_v2.pth (59 MB)	Trained on paired images (UIEB and LSUI training splits) for stronger colour and haze correction. 29 M parameters, best on a GPU.

Usage

git clone https://github.com/alzayats/UDnet.git && cd UDnet
pip install -r requirements.txt
python Inference.py --input path/to/images --output results/                    # V1
python Inference.py --input path/to/images --output results/ --model v2         # V2 (preview)
python Inference.py --input path/to/images --output results/ --model v2 --ai x2 # V2 + AI detail, 2x upscaled

The weights are downloaded from this repository automatically on first use. --ai adds a Real-ESRGAN pass that sharpens and upscales; it can invent fine texture, so use it for viewing, not measurement.

Files

File	Description
UDnet.pth	V1 inference weights (PyTorch state_dict, 1.4 M parameters)
UDnet_v2.pth	V2 inference weights ({'state_dict', 'config'}, float16, 29 M parameters)

V2 is a preview and is intended for research use.

Citation

@article{SALEH2025126314,
  title   = {Adaptive deep learning framework for robust unsupervised underwater image enhancement},
  journal = {Expert Systems with Applications},
  volume  = {268},
  pages   = {126314},
  year    = {2025},
  doi     = {https://doi.org/10.1016/j.eswa.2024.126314},
  author  = {Alzayat Saleh and Marcus Sheaves and Dean Jerry and Mostafa {Rahimi Azghadi}},
}