Image Inpainting via Generative Multi-column Convolutional Neural Networks
Yi Wang, Xin Tao, Xiaojuan Qi, Xiaoyong Shen, Jiaya Jia
Abstract
In this paper, we propose a generative multi-column network for image inpainting. This network synthesizes different image components in a parallel manner within one stage. To better characterize global structures, we design a confidence-driven reconstruction loss while an implicit diversified MRF regularization is adopted to enhance local details. The multi-column network combined with the reconstruction and MRF loss propagates local and global information derived from context to the target inpainting regions. Extensive experiments on challenging street view, face, natural objects and scenes manifest that our method produces visual compelling results even without previously common post-processing.
BibTeX
@inproceedings{NEURIPS2018_6f3ef77a,
author = {Wang, Yi and Tao, Xin and Qi, Xiaojuan and Shen, Xiaoyong and Jia, Jiaya},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Image Inpainting via Generative Multi-column Convolutional Neural Networks},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/6f3ef77ac0e3619e98159e9b6febf557-Paper.pdf},
volume = {31},
year = {2018}
}