期刊名称：International Journal of Multimedia and Ubiquitous Engineering
印刷版ISSN：1975-0080
出版年度：2016
卷号：11
期号：10
页码：273-284
出版社：SERSC
摘要：Since cases being analyzed and detected by the police using surveillance videos, insufficient resolution and degradation always lead to the suspect’s face being unrecognized. For such cases, from the perspective of learning-based face super-resolution method, this paper proposed a novel face restored solution based on face super-resolution and image mosaic. Firstly, by applying global similarity selection over the entire database, the scope of the used samples can be narrowed and boost the speed. Then the local representation between neighbors can make the method more robust against noise under severe conditions. Finally the reconstructed face could be inserted into original background by the improved Poisson Editing algorithm. This scheme achieve the effect of face restoring on a single frame in surveillance videos. Experiments under common database and real surveillance video scenarios demonstrate our method has better performance, especially for noise input images.
关键词：face super-resolution; global similarity selection; local similarity ;representation; position-patch; surveillance videos; Poisson Editing
