This repository provides the official PyTorch implementation of the following paper:

Unsupervised-Cross-Domain-Learning-for-Noise-Removal-from-a-Single-Image

To install requirements:

conda env create -n [your env name] -f environment.yaml
conda activate [your env name]

1. Download DIV2K dataset for training in here
2. Randomly split the DIV2K dataset into Clean/Noisy set. Please refer the .txt files in split_data.
3. Place the splitted dataset(DIV2K_C and DIV2K_N) in ./dataset directory.

dataset
└─── DIV2K_C
└─── DIV2K_N
└─── test

4. Use gen_dataset_synthetic.py to package dataset in the h5py format.
5. After that, run this command:

sh ./scripts/train_awgn_sigma15.sh # AWGN with a noise level = 15
sh ./scripts/train_awgn_sigma25.sh # AWGN with a noise level = 25
sh ./scripts/train_awgn_sigma50.sh # AWGN with a noise level = 50

6. After finishing the training, .pth file is stored in ./exp/[exp_name]/[seed_number]/saved_models/ directory.

1. Download SIDD-Medium Dataset for training in here
2. Radnomly split the SIDD-Medium Dataset into Clean/Noisy set. Please refer the .txt files in split_data.
3. Place the splitted dataset(SIDD_C and SIDD_N) in ./dataset directory.

dataset
└─── SIDD_C
└─── SIDD_N
└─── test

4. Use gen_dataset_real.py to package dataset in the h5py format.
5. After that, run this command:

sh ./scripts/train_real.sh

6. After finishing the training, .pth file is stored in ./exp/[exp_name]/[seed_number]/saved_models/ directory.

dataset
└─── LDCT_C
└─── LDCT_N
└─── test

1. Use gen_dataset_real.py to package dataset in the h5py format.
2. After that, run this command:

sh ./scripts/train_ldct.sh

1. Download CBSD68 dataset for evaluation in here
2. Place the dataset in ./dataset/test directory.

dataset
└─── train
└─── test
     └─── CBSD68
     └─── SIDD_test
     └─── LDCT_test

3. After that, run this command:

sh ./scripts/test_awgn_sigma15.sh # AWGN with a noise level = 15
sh ./scripts/test_awgn_sigma25.sh # AWGN with a noise level = 25
sh ./scripts/test_awgn_sigma50.sh # AWGN with a noise level = 50

1. Download the SIDD test dataset for evaluation in here
2. Place the dataset in ./dataset/test directory.

dataset
└─── train
└─── test
     └─── CBSD68
     └─── SIDD_test
     └─── LDCT_test

3. After that, run this command:

sh ./scripts/test_real.sh

1. Download the LDCT test dataset for evaluation in here
2. Place the dataset in ./dataset/test directory.

dataset
└─── train
└─── test
     └─── CBSD68
     └─── SIDD_test
     └─── LDCT_test

3. After that, run this command:

sh ./scripts/test_ldct.sh

We provide pre-trained models in ./checkpoints directory.

checkpoints
|   best_sigma15.pth # pre-trained model (AWGN with a noise level = 15)
|   best_sigma25.pth # pre-trained model (AWGN with a noise level = 25)
|   best_sigma50.pth # pre-trained model (AWGN with a noise level = 50)
|   best_SIDD.pth # pre-trained model (Real-World noise)
|   best_LDCT.pth # pre-trained model (LDCT noise)

This code is built on UID-FDK,U-GAT-IT,CARN, SSD-GAN. We thank the authors for sharing their codes.

If you find these codes useful, please cite our related papers, shown as follows.

[1] H.-X. Tsai and L.-W. Kang, "Unsupervised cross domain learning for noise removal from a single image," Proc. IEEE International Conference on Imaging Systems and Techniques, Kaohsiung, Taiwan, 2022.

[2] H.-X. Tsai and L.-W. Kang, "Cross domain deep learning for noise removal from LDCT images," Proc. IEEE International Conference on Consumer Electronics-Taiwan, Taipei, Taiwan, 2022, pp. 475-476.

If you have any questions, feel free to contact me