A Survey of Image Super-Resolution Reconstruction

被引：0
|
作者：
Tang Y.-Q. [1 ]
Pan H. [1 ]
Zhu Y.-P. [2 ]
Li X.-D. [1 ]
机构：
[1] School of Automation, Southeast University, Nanjing, 210096, Jiangsu
[2] School of Information and Communication Engineering, Communication University of China, Beijing
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 07期
关键词：
Deep learning; Image processing; Image super-resolution reconstruction; Methods review;
D O I：
10.3969/j.issn.0372-2112.2020.07.022
中图分类号：
学科分类号：
摘要：
Image super-resolution reconstruction (SR) aims to obtain high-resolution images from one or more low-resolution images.Recently, SR has been developing and widely applied in different fields.This survey retrospects the history of SR technique and provides a comprehensive overview of representative SR methods, with an emphasis on recent deep learning-based approaches.We elaborate the details of various deep learning-based SR methods, including their strengths and weakness, in terms of the deep learning model, architecture, and message pass.Finally, we discuss the possible research directions on SR technique. © 2020, Chinese Institute of Electronics. All right reserved.
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页码：1407 / 1420
页数：13
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