Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (20): 1-12.DOI: 10.3778/j.issn.1002-8331.2303-0111

• Research Hotspots and Reviews • Previous Articles     Next Articles

Image Inpainting Algorithm Based on Deep Neural Networks

LYU Jianfeng, SHAO Lizhen, LEI Xuemei   

  1. 1.School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
    2.Shunde Innovation School, University of Science and Technology Beijing, Foshan, Guangdong 528399, China
    3.Office of Information Technology, University of Science and Technology Beijing, Beijing 100083, China
  • Online:2023-10-15 Published:2023-10-15



  1. 1.北京科技大学 自动化学院,北京 100083
    2.北京科技大学 顺德创新学院,广东 佛山 528399
    3.北京科技大学 信息化建设与管理办公室,北京 100083

Abstract: With the rapid development of deep learning, computer vision technology is applied more and more widely. At the same time, the image inpainting technology based on the known information of the damaged image using deep neural network has also become a hot topic. The image inpainting methods based on depth neural network in recent years are reviewed and analyzed. Firstly, the image inpainting methods are classified and summarized according to the view of model optimization. Then the common datasets and performance evaluation indicators are introduced, and the performance evaluation and analysis of various deep neural network-based image inpainting algorithms are carried out on the relevant data sets. Finally, the challenges faced by the existing image inpainting methods are analyzed, and the future research works are prospected.

Key words: deep neural networks, image inpainting, algorithm analysis

摘要: 深度学习的快速发展使计算机视觉技术应用越来越广泛,同时利用深度神经网络根据破损图像的已知信息对图像复原的修复技术成为关注的热点。对近年基于深度神经网络的图像修复方法进行了综述和分析:按照模型优化的方向,对图像修复方法进行分类综述;介绍了图像修复常用的数据集和性能评价指标,并在相关数据集上对各种基于深度神经网络的破损图像修复算法进行性能评价和分析;总结和分析了现有图像修复方法面临的挑战和未来研究方向。

关键词: 深度神经网络, 图像修复, 算法分析