期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：252
期号：4
页码：1-9
DOI：10.1088/1755-1315/252/4/042121
出版社：IOP Publishing
摘要：Video or image super-resolution technology is designed to recovery a high-resolution image from a low-resolution video or image. In recent years, deep neural networks have developed rapidly and have been applied in many digtal image processing tasks. In this paper, we choose an optical flow network to effectively exploit temporal relation within multiple consecutive video frames. In addition, we propose a weight distribution network which gives weight images of different high-resolution images obtained by various super-resolution network methods. This architecture combines advantages of different methods and provides a more accurate high-resolution image. We build a dataset with high-definition video, and use this dataset to train and test our networks. We compare our algorithm with other super-resolution methods and show that it performs a state-of-the-art results.
