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	A fast super-resolution method based on sparsity properties
	Bai, Yuanchao ; Jia, Huizhu ; Xie, Xiaodong ; Chen, Rui ; Jiang, Ming ; Gao, Wen
	2015
英文摘要	Super-resolution enhancement is a kind of promising approach to enhance the spatial resolution of images. To super-resolve a satisfying result, regularization term design and blur kernel estimation are two important aspects which need to be carefully considered. In this paper, we propose a robust regularized super-resolution reconstruction approach based on two sparsity properties to deal with these two aspects. Firstly, we design a sparse reweighted TV L1 prior to restrict the first derivative of the upsampled image. Then, noticing that only deblurring sparse high gradient areas can sharpen the super-resolution result, we design an over-deblurring control method to decrease the artifacts caused by inaccurate blur kernel estimation. We also design a fast optimization algorithm to solve our model. The experimental results show that the proposed approach achieves a remarkable performance both in visual quality and run time. ? 2015 IEEE.; EI
语种	英语
出处	Visual Communications and Image Processing, VCIP 2015
DOI标识	10.1109/VCIP.2015.7457866
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/449491]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Bai, Yuanchao,Jia, Huizhu,Xie, Xiaodong,et al. A fast super-resolution method based on sparsity properties. 2015-01-01.

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