Awesome
EII: Image Inpainting with External-Internal Learning and Monochromic Bottleneck
Image Inpainting with External-Internal Learning and Monochromic Bottleneck
Tengfei Wang*, Hao Ouyang*, Qifeng Chen
CVPR 2021
paper | project website | video
Introduction
The proposed method can be applied to improve the color consistency of leaning-based image inpainting results. The progressive internal color propagation shows strong performance even with large mask ratios. <img src="pics/color.jpg" height="305px"/> <img src="pics/multi-ratio.jpg" height="360px"/>
Prerequisites
- Python 3.6
- Pytorch 1.6
- Numpy
Installation
git clone https://github.com/Tengfei-Wang/external-internal-inpainting.git
cd external-internal-inpainting
Quick Start
Colorization
To try our internal colorization method:
python main.py --img_path images/input2.png --gray_path images/gray2.png --mask_path images/mask2.png --pyramid_height 3
The colorization results are placed in ./results.
In case the colorization results are unsatisfactory, you may consider changing the pyramid_height (2~5 work well for most cases).
Reconstruction
For the monochromic reconstruction stage, multiple inpainting networks can be applied as backbones by modifying the original input image, like:
input_new = torch.concat([input_RGB, input_gray],1) #input_new is 4-channel
output = backbone_model(input_new, mask) #output is single-channel
loss = criterion(output, input_gray)
Citation
If you find this work useful for your research, please cite:
@inproceedings{wang2021image,
title={Image Inpainting with External-internal Learning and Monochromic Bottleneck},
author={Wang, Tengfei and Ouyang, Hao and Chen, Qifeng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={5120--5129},
year={2021}
}
Contact
Please send emails to tengfeiwang12@gmail.com or ououkenneth@gmail.com if there is any question
Acknowledgement
We thank the authors of DIP for sharing their codes.