【摘要】 近年来,生成式对抗网络(GAN)为图像增强提供了新的技术和手段,具有比传统深度学习更强大的特征学习和表达能力,在图像增强领域取得了显著成功。文章首先介绍了GAN模型的基本思想和原理,分析了GAN各个变体改进的方式及优缺点;其次从图像质量提高、图像生成、图像补全和其他图像处理的应用等方面分析了GAN应用于图像增强的研究现状;最后归纳总结了GAN模型与其在图像增强中面临的问题,并对问题的解决方案及未来应用进行了总结展望。更多还原

【Abstract】 In recent years, generative adversarial networks(GAN) has provided new techniques and means for image enhancement. It has more powerful feature learning and expression capabilities than traditional deep learning, and has achieved remarkable success in the field of image enhancement. Firstly, the basic ideas and principles of GAN model are introduced, and the improvement methods, advantages and disadvantages of GAN variants are analyzed. Secondly, the research status of GAN applied to image enhancement is analyzed from the aspects of image quality improvement, image generation, image complementation and other image processing applications. Finally, the GAN model and the problems in image enhancement are summarized and summarized, and the solution and future application of the problem are summarized.更多还原