Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
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Article Number | 01020 | |
Number of page(s) | 9 | |
Section | Traffic Prediction and Analysis | |
DOI | https://doi.org/10.1051/itmconf/20257001020 | |
Published online | 23 January 2025 |
Research Developments in Generative Adversarial Networks for Image Restoration and Communication
Chengdu College of University of Electronic Science and Technology of China, Western High-Tech District, Chengdu 611731, China
Corresponding author: 1809040311@stu.hrbust.edu.cn
Generative Adversarial Networks (GAN) is always a popular study topic in artificial intelligence. This paper will analyze the principle of the GAN and introduce the development of the GAN and various derivative models. The improved Super-Resolution GAN (SRGAN) model and Cascading Residual Super-Resolution GAN (CR-SRGAN) model based on the GAN model achieve super-resolution of dark and old artifact images and solve the problem of color restoration and texture enrichment of dark and old artifacts. The GAN model is also widely used in the field of communication and information security. It proposes an End-to-End(E-to-E) communication encryption system based on Deep Convolutional GAN (DCGAN) to solve the secure transmission problem in wireless communication systems based on E-to-E learning. The system can realize encoding and decoding of input bits of arbitrary length with good generalization ability. Finally, the image restoration and communication encryption are summarized, along with an outlook on their development trends.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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