numpy==1.14.4
Pillow==5.1.0
six==1.11.0
tensorboardX==1.2
torch==0.4.1
torchvision==0.2.1
tqdm==4.23.4
To generate binary masks, use
python generate_data.py
To generate the image covered by the mask, that is, generate the simulated damaged image, use
python 1test.py
To conduct network model training, use
python train.py
The image data set and mask data set can be simply modified at the beginning of the code as required.
To generate a repair image, use
python 2test.py
If you find our code or paper useful, please cite the paper:
@article{PengWZ23,
title = {C3N: Content-constrained convolutional network for mural image completion},
author = {Xianlin Peng, Huayu Zhao, Xiaoyu Wang, Yongqin Zhang, Zhan Li, Qunxi Zhang, Jun Wang, Jinye Peng, Haida Liang},
journal = {Neural Computing and Applications},
volume = {35},
article id = {},
pages = {1959-1970},
year={2023}
}