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	Improved total variation based image compressive sensing recovery by nonlocal regularization
	Zhang, Jian ; Liu, Shaohui ; Xiong, Ruiqin ; Ma, Siwei ; Zhao, Debin
	2013
英文摘要	Recently, total variation (TV) based minimization algorithms have achieved great success in compressive sensing (CS) recovery for natural images due to its virtue of preserving edges. However, the use of TV is not able to recover the fine details and textures, and often suffers from undesirable staircase artifact. To reduce these effects, this paper presents an improved TV based image CS recovery algorithm by introducing a new nonlocal regularization constraint into CS optimization problem. The nonlocal regularization is built on the well known nonlocal means (NLM) filtering and takes advantage of self-similarity in images, which helps to suppress the staircase effect and restore the fine details. Furthermore, an efficient augmented Lagrangian based algorithm is developed to solve the above combined TV and nonlocal regularization constrained problem. Experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art TV based algorithm in both PSNR and visual perception. ? 2013 IEEE.; EI; 0
语种	英语
DOI标识	10.1109/ISCAS.2013.6572469
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/410733]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhang, Jian,Liu, Shaohui,Xiong, Ruiqin,et al. Improved total variation based image compressive sensing recovery by nonlocal regularization. 2013-01-01.

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