Topic: Applications of Generative Adversarial Networks in Computer Vision and Image Processing

This Special Issue aims to explore the applications and research progress of generative adversarial networks (GAN) in the fields of computer vision and image processing. GAN, as a powerful artificial intelligence model, improves its performance through adversarial training between the generator and discriminator. It has demonstrated great capacity for feature learning and pattern classification in image recognition and generation tasks, such as forgery image detection, image segmentation, image enhancement, image restoration, and image style transfer. Since its inception, GAN has inspired the creation of several improved versions that have attracted significant attention, such as conditional generative adversarial network (CGAN), BigGAN, StyleGAN, and CycleGAN. Recently, the performance of the GAN-based model has been further promoted by new techniques, such as transformer and diffusion models, to generate high-quality and diverse images. Notably, GAN is also used to generate adversarial samples for deep learning models, which is a potential threat for their applications. This Special Issue focuses on algorithms, theories, models, and applications of GAN in computer vision and image processing, together with the latest advancements and future directions in related research. Potential topics include but are not limited to the following:

● Applications of GAN in computer vision;

● Applications of GAN in image processing;

● Applications of GAN in intelligent security;

● Applications of GAN in video generation and video processing;

● Applications of GAN in computer-aided design and artistic creation;

● Applications of GAN in robotics;

● Industrialized applications of GAN;

● GAN-generated content;

● Semi-supervised and unsupervised GAN;

● GAN for embedded systems.