1．南京信息工程大学 电子与信息工程学院 南京 210044; 2．南京信息工程大学 江苏省大气环境与装备技术协同创新中心 南京210044
TP183
江苏省333高层次人才培养工程(2625)
Chen Xiao
1.School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China1.School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
图像超分辨率是指从低分辨率图像生成对应的高分辨率图像。它在人脸识别、数字高清电视、视频通信等领域应用广泛。为了对图像超分辨率技术进行深入探索和总结，本文首先回顾了单图像超分辨率的历史，综述了基于非学习的超分辨率方法，其中展开介绍了基于插值和基于重建的方法，接着重点介绍了基于学习的方法，详细分析了基于深度学习的图像超分辨率，具体总结了SRCNN、ESPCN、SRGAN这三种图像超分辨率方法，并将其与递归结构、密集结构、注意力机制网络结构进行对比，之后分析了损失函数和上采样方式在图像超分辨率中的作用，介绍了常用数据集和图像评价指标，展示了图像超分辨率的可视化结果。最后，总结了现有单图像超分辨率方法的进展和不足。
Image super-resolution (SR) reconstruction refers to the generation of a corresponding high Resolution image from a low Resolution image. SR has important application value in monitoring, remote sensing, digital HD, video coding communication and other fields. In this paper, we first review the history of single image SR and summarize the non-learning based SR methods. Among them, interpolation and reconstruction based methods are introduced, and then learning-based methods are introduced, in which SR based on deep learning is analyzed such as SRCNN, ESPCN and SRGAN are summarized in detail, and compared with recursive structure, dense structure and attention mechanism network structure. Then the function of loss function and upsampling method in image SR is analyzed, common data sets and image evaluation indexes are introduced, and visualization results of image SR are displayed. Finally, the progress and deficiency of single image super-resolution technology are summarized.
陈 晓,荆茹韵.单图像超分辨率方法综述[J].电子测量技术,2022,45(9):104-112
复制
