This repository contains the PyTorch implementation for training and testing HCLR-Net, a model designed for underwater image enhancement. If you find this code useful, please consider citing our paper and starring this repository.
pip install pytorch_lightning
python train.py
../tb_logs/UCR/version_0/checkpoints
directory.Training data:
./Datasets/train/input
folder../Datasets/train/gt
folder.Validation data:
./Datasets/val/input
folder../Datasets/val/input
folder.test_images
folder.python test.py
.git clone https://github.com/Hikari0608/Eval
.git checkout 4bc231309262d9aba9aa6b811ef267f9cc702633
.val.py
.bash eval.sh
.For your convenience, we provide all paired training data and testing data used in our paper. You can download the pre-trained weights from the following link:
Please feel free to reach out if you encounter any issues or have any questions regarding the usage of our code. We hope this repository proves helpful for your research and applications in underwater image enhancement.