Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)

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GANs in Image Generation and Denoising: From Infrastructure to Real-World Applications

Authors
Haoyang Sun1, *
1School of Information Engineering, Minzu University of China, Beijing, China
*Corresponding author. Email: albertmusk6@gmail.com
Corresponding Author
Haoyang Sun
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_65How to use a DOI?
Keywords
GAN; Image Generation; Image Denoising
Abstract

Generative Adversarial Networks (GANs) are currently the mainstream generative models, widely used in image, audio, and other data processing. Over the course of ten years, many GAN models have emerged that are adapted to different use cases. With the constant improvement of GAN model structure, they have achieved very good performance in image reconstruction and resolution enhancement. Image generation and image denoising are currently two core application scenarios in the field of artificial intelligence and are widely used in the fields of medicine and art. Image generation depends on denoising technology to improve quality, and image denoising depends on generation technology to broaden its space. Two kinds of tasks often cooperate. This article will systematically review the development and application of GAN in image generation and image denoising, introduce typical GAN variant and technological evolution in two application scenarios, analyze their technological innovation and applicable scenario. Finally, looking forward to the possible development trend in the future. The importance of this part is that the article focuses on the two main core application scenarios of GAN in the image field to trace the technological development, so as to help readers select an appropriate technical solution.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
Series
Advances in Computer Science Research
Publication Date
24 April 2026
ISBN
978-94-6239-648-7
ISSN
2352-538X
DOI
10.2991/978-94-6239-648-7_65How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Haoyang Sun
PY  - 2026
DA  - 2026/04/24
TI  - GANs in Image Generation and Denoising: From Infrastructure to Real-World Applications
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 596
EP  - 604
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_65
DO  - 10.2991/978-94-6239-648-7_65
ID  - Sun2026
ER  -