Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (10): 2193-2218.DOI: 10.3778/j.issn.1673-9418.2204101

• Surveys and Frontiers • Previous Articles     Next Articles

Survey of Research on Image Inpainting Methods

LUO Haiyin1,2,+(), ZHENG Yuhui1,2   

  1. 1. School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, China
    2. Engineering Research Center of Digital Forensics Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Received:2022-04-06 Revised:2022-06-09 Online:2022-10-01 Published:2022-10-14
  • About author:ZHENG Yuhui, born in 1982, Ph.D., professor, member of CCF. His research interests include computer vision, pattern recognition, etc.
    ZHENG Yuhui, born in 1982, Ph.D., professor, member of CCF. His research interests include computer vision, pattern recognition, etc.
  • Supported by:
    National Natural Science Foundation of China(61972206);National Natural Science Foundation of China(62011540407);Six Talent Peaks Project in Jiangsu Province(RJFW-015)


罗海银1,2,+(), 郑钰辉1,2   

  1. 1.南京信息工程大学 计算机学院、软件学院、网络空间安全学院,南京 210044
    2.南京信息工程大学 数字取证教育部工程研究中心,南京 210044
  • 通讯作者: + E-mail:
  • 作者简介:郑钰辉(1982—),男,山西芮城人,博士,教授,CCF会员,主要研究方向为计算机视觉、模式识别等。
  • 基金资助:


Image inpainting refers to restoring the pixels in damaged areas of an image to make them as consistent as possible with the original image. Image inpainting is not only crucial in the computer vision tasks, but also serves as an important cornerstone of other image processing tasks. However, there are few researches related to image inpainting. In order to better learn and promote the research of image inpainting tasks, the classic image inpainting algorithms and representative deep learning image inpainting methods in the past ten years are reviewed and analyzed. Firstly, the classical traditional image inpainting methods are briefly summarized, and divided into partial differential equation-based and sample-based image inpainting methods, and the limitations of traditional image methods are further analyzed. Deep learning image inpainting methods are divided into single image inpainting and pluralistic image inpainting according to the number of output images of the model, and different methods are analyzed and summarized in combination with application images, loss functions, types, advantages, and limitations. After that, the commonly used datasets and quantitative evaluation indicators of image inpainting methods are described in detail, and the quantitative data of image inpainting methods to inpaint damaged areas of different areas on different image datasets are given. According to the quantitative data, the performance of image inpainting methods based on deep learning is compared and analyzed. Finally, the limitations of existing image inpainting methods are summarized and analyzed, and new ideas and prospects for future key research directions are proposed.

Key words: computer vision, image inpainting, deep learning, single image inpainting, pluralistic image inpainting



关键词: 计算机视觉, 图像修复, 深度学习, 单元图像修复, 多元图像修复

CLC Number: