Zida Chen*, Ziran Zhang*, Haoying Li, Menghao Li, Yueting Chen, Qi Li, Huajun Feng, Zhihai Xu, Shiqi Chen

State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University

2024.01.17 Our train/test code, LAP dataset and pre-trained model are available now.

2023.12.09 Our paper is accepted by AAAI 2024! Paper Link

You can download our synthetic LAP training and evaluation dataset.

Please unzip them and config the dataset path in your config file (e.g. options/test_JARNet_LAP.yaml).

Note: You need to reserve at least 200GB of disk space for storage of data.

• Install the packages in your environment (python >= 3.7):

pip install -r requirements.txt

• Build up BasicSR environment by running:

python setup.py develop --no_cuda_ext

• To choose the gpu id, please modify the following code in the train/evaluation script:

os.environ["CUDA_VISIBLE_DEVICES"] = {your gpu id}

You can train the JARNet by using:

python basicsr/train_jarnet.py -opt options/train_JARNet_LAP.yml

You can train other restoration model (e.g. NAFNet) by using:

python basicsr/train_others.py -opt options/train_NAFNet_LAP.yml

You should fill out the path of the dataset in train config (i.e. yaml) file.

You can evaluate the JARNet by using:

python basicsr/test.py -opt options/test_JARNet_LAP.yml

You should fill out the path of the dataset and pre-trained model in test config (i.e. yaml) file.

You can infer JARNet by the pre-trained model on our LAP evaluation dataset.

If this repo helps you, please consider citing our work:

@inproceedings{chen2024deep,
  title={Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration Network},
  author={Zida Chen and Ziran Zhang and Haoying Li and Menghao Li and Yueting Chen and Qi Li and Huajun Feng and Zhihai Xu and Shiqi Chen},
  booktitle={AAAI},
  year={2024}
}

If you have any question, please contact [email protected].

Our code is based on the BasicSR toolbox.