A Review of Image Inpainting Automation Based on Deep Learning. Issue 1 (1st February 2022)

• Record Type: Journal Article
• Authors: Sun, Qi; Zhai, Rui; Zuo, Fang; Zhong, Yuhao; Zhang, Yutao
• Is Part Of: Journal of physics. Volume 2203:Issue 1(2022)
• Publication Date: 2022-02-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2203/1/012037
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: 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.