TANG Yan-qiu1, PAN Hong1, ZHU Ya-ping2, LI Xin-de1
1. School of Automation, Southeast University, Nanjing, Jiangsu 210096, China;
2. School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
Abstract: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.
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