Open AccessA Review of Image Inpainting Automation Based on Deep Learning
Author(s) -
Qing Sun,
Rui Zhai,
Fang Zuo,
Yuhao Zhong,
Yutao Zhang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2203/1/012037
Subject(s) - inpainting , artificial intelligence , automation , deep learning , image (mathematics) , computer science , key (lock) , strengths and weaknesses , computer vision , machine learning , engineering , psychology , mechanical engineering , social psychology , computer security
The purpose of image inpainting is to automatically repair damaged areas using relevant information from preserved areas. Recent years, with the advancement of deep learning, significantly improved performance of image drawing has been achieved. In this paper, we are committed to reviewing the key techniques for automating image inpainting research. The article briefly describes conventional methods while focusing on deep learning-based inpainting methods, including model classification, strengths and weaknesses, range of usage, and performance comparison. Finally, the current issues and tendencies of image inpainting automation are discussed and predicted.