期刊
PATTERN RECOGNITION
卷 51, 期 -, 页码 59-71出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2015.08.008
关键词
Video super-resolution; Superpixel; Auto-regressive model; Spatio-temporal correlation
资金
- National Basic Research Program of China [2013CB329301]
- NSF of China [61302059, 61372084, 61373703]
- Tianjin Research Program of Application Foundation and Advanced Technology [13JCQNJC03900, 12JCYBJC10300]
This paper proposes a video super-resolution method based on an adaptive superpixel-guided auto-regressive (AR) model. Key-frames are automatically selected and super-resolved by a sparse regression method. Non-key-frames are super-resolved by exploiting the spatio-temporal correlations: the temporal correlation is exploited by an optical flow method while the spatial correlation is modeled by a superpixel-guided AR model. Experimental results show that the proposed method outperforms state-of-the-art methods in terms of both subjective visual quality and objective peak signal-to-noise ratio (PSNR). The proposed method requires less computation and is suitable for practical applications. Crown Copyright (C) 2015 Published by Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据