Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 5 , 31 May 2023


Open Access | Article

Generative adversarial network based image inpainting

Jingjing Gao * 1
1 Beijing University of Technology, No.100 Ping Le Yuan, Chaoyang District, Beijing City, PR China, 100124.

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 5, 93-98
Published 31 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Jingjing Gao. Generative adversarial network based image inpainting. ACE (2023) Vol. 5: 93-98. DOI: 10.54254/2755-2721/5/20230540.

Abstract

Image inpainting, which is the repair of pixels in damaged areas of an image to make it look as much like the original image as possible. Deep learning-based image inpainting technology is a prominent area of current research interest. This paper focuses on a systematic and comprehensive study of GAN-based image inpainting and presents an analytical summary. Firstly, this paper introduces GAN, which includes the principle of GAN and its mathematical expression. Secondly, the recent GAN-based image inpainting algorithms are summarized, and the advantages and disadvantages of each algorithm are listed. After that, the evaluation metrics, and common datasets of deep learning-based image inpainting are listed. Finally, the existing image inpainting methods are summarized and summarized, and the ideas for future key research directions are presented and prospected.

Keywords

computer vision, image inpainting, deep learning, Generative Adversarial Networks(GAN)

References

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5. YEH R A, CHEN C, YIAN LIM T, et al. (2017). Semantic image inpainting with deep generative models[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Honolulu, HI, USA: 5485-5493.

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-57-7
ISBN (Online)
978-1-915371-58-4
Published Date
31 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/5/20230540
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated