计算机科学与探索2024，Vol.18Issue(3)：553-573,21.DOI:10.3778/j.issn.1673-9418.2307073
生成对抗网络在图像修复中的应用综述
Review of Application of Generative Adversarial Networks in Image Restoration
摘要
Abstract
With the rapid development of generative adversarial networks,many image restoration problems that are difficult to solve based on traditional methods have gained new research approaches.With its powerful generation ability,generative adversarial networks can restore intact images from damaged images,so they are widely used in image restoration.In order to summarize the relevant theories and research on the problem of using generative ad-versarial networks to repair damaged images in recent years,based on the categories of damaged images and their adapted repair methods,the applications of image restoration are divided into three main aspects:image inpainting,image deblurring,and image denoising.For each aspect,the applications are further subdivided through technical principles,application objects and other dimensions.For the field of image inpainting,different image completion methods based on generative adversarial networks are discussed from the perspectives of using conditional guidance and latent coding.For the field of image deblurring,the essential differences between motion blurred images and static blurred images and their repair methods are explained.For the field of image denoising,personalized denoising methods for different categories of images are summarized.For each type of applications,the characteristics of the specific GAN models employed are summarized.Finally,the advantages and disadvantages of GAN applied to image restoration are summarized,and the future application scenarios are prospected.关键词
图像修复/生成对抗网络/图像补全/图像去模糊/图像去噪Key words
image restoration/generative adversarial network/image inpainting/image deblurring/image denoising分类
信息技术与安全科学引用本文复制引用
龚颖,许文韬,赵策,王斌君..生成对抗网络在图像修复中的应用综述[J].计算机科学与探索,2024,18(3):553-573,21.基金项目
中国人民公安大学网络空间安全执法技术双一流创新研究专项(2023SYL07).This work was supported by the Double First-Class Innovation Research Project for People's Public Security University of China(2023SYL07). （2023SYL07）
