This is the implementation of manuscript "Wide & deep learning for spatial & intensity adaptive image restoration".
- Ubuntu 18.04+ or Windows 10/11
- NVIDIA GPU + CUDA (Geforce RTX 3090-Ti with 24GB memory, CUDA 11.1 was tested)
- Python 3.7+
- Pytorch 1.7.0+
We provided very small version of the constructed
denoising and deturbulence dataset, deposited in
./denoising/data/
and ./deturbulence/data/
.
- Run
python test.py
in each sub-folder to run the pre-trained models. The restored results will be saved in./results/
of each sub-folder. - Run
python evaluate.py
in each sub-folder to obtain the quantitative evaluations of the restored results. As very little data is provided here, the evaluation results will not be the same as in manuscript.
- Run
python train.py
to perform training with the default setting.
Please cite our paper in your publications if our work helps your research.
@article{wang2023wide,
title={Wide \& deep learning for spatial \& intensity adaptive image restoration},
author={Wang, Yadong and Bai, Xiangzhi},
journal={arXiv preprint arXiv:2305.18708},
year={2023}
}