Image Inpainting with External-Internal Learning and Monochromic Bottleneck
Abstract
Although recent inpainting approaches have demonstrated significant improvement with deep neural networks, they still suffer from artifacts such as blunt structures and abrupt colors when filling in the missing regions. To address these issues, we propose an external-internal inpainting scheme with a monochromic bottleneck that helps image inpainting models remove these artifacts. In the external learning stage, we reconstruct missing structures and details in the monochromic space to reduce the learning dimension. In the internal learning stage, we propose a novel internal color propagation method with progressive learning strategies for consistent color restoration. Extensive experiments demonstrate that our proposed scheme helps image inpainting models produce more structure-preserved and visually compelling results.
Cite
Text
Wang et al. "Image Inpainting with External-Internal Learning and Monochromic Bottleneck." Conference on Computer Vision and Pattern Recognition, 2021. doi:10.1109/CVPR46437.2021.00508Markdown
[Wang et al. "Image Inpainting with External-Internal Learning and Monochromic Bottleneck." Conference on Computer Vision and Pattern Recognition, 2021.](https://mlanthology.org/cvpr/2021/wang2021cvpr-image/) doi:10.1109/CVPR46437.2021.00508BibTeX
@inproceedings{wang2021cvpr-image,
title = {{Image Inpainting with External-Internal Learning and Monochromic Bottleneck}},
author = {Wang, Tengfei and Ouyang, Hao and Chen, Qifeng},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2021},
pages = {5120-5129},
doi = {10.1109/CVPR46437.2021.00508},
url = {https://mlanthology.org/cvpr/2021/wang2021cvpr-image/}
}