Structure-guided image completion via regularity statistics
Shuai Yang, Jiaying Liu, Sijie Song, Mading Li, Zongming Quo
Abstract
In this paper, we propose a novel hierarchical image completion approach using regularity statistics, considering structure features. Guided by dominant structures, the target image is used to generate reference images in a self-reproductive way by image data enhancement. The structure-guided image data enhancement allows us to expand the search space for samples. A Markov Random Field model is used to guide the enhanced image data combination to globally reconstruct the target image. For lower computational complexity and more accurate structure estimation, a hierarchical process is implemented. Experiments demonstrate the effectiveness of our method comparing to several state-of-the-art image completion techniques.
BibTeX
@inproceedings{icassp2016_structureguidedi,
title = {Structure-guided image completion via regularity statistics},
author = {Shuai Yang and Jiaying Liu and Sijie Song and Mading Li and Zongming Quo},
booktitle = {ICASSP 2016},
year = {2016}
}