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.

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 [email protected] or [email protected] if there is any question

Acknowledgement

We thank the authors of DIP for sharing their codes.