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	Image super-resolution via dual-dictionary learning and sparse representation
	Zhang, Jian ; Zhao, Chen ; Xiong, Ruiqin ; Ma, Siwei ; Zhao, Debin
	2012
英文摘要	Learning-based image super-resolution aims to reconstruct high-frequency (HF) details from the prior model trained by a set of high-and low-resolution image patches. In this paper, HF to be estimated is considered as a combination of two components: main high-frequency (MHF) and residual high-frequency (RHF), and we propose a novel image super-resolution method via dual-dictionary learning and sparse representation, which consists of the main dictionary learning and the residual dictionary learning, to recover MHF and RHF respectively. Extensive experimental results on test images validate that by employing the proposed two-layer progressive scheme, more image details can be recovered and much better results can be achieved than the state-of-the-art algorithms in terms of both PSNR and visual perception. ? 2012 IEEE.; EI; 0
语种	英语
DOI标识	10.1109/ISCAS.2012.6271583
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/412268]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhang, Jian,Zhao, Chen,Xiong, Ruiqin,et al. Image super-resolution via dual-dictionary learning and sparse representation. 2012-01-01.

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