Denoising-Based Turbo Compressed Sensing

doi:10.1109/ACCESS.2017.2697978

	Denoising-Based Turbo Compressed Sensing
	Zhipeng Xue1 ; Junjie Ma 2; Xiaojun Yuan1,3
	2017
发表期刊	IEEE ACCESS (IF:3.4[JCR-2023],3.7[5-Year])
ISSN	2169-3536
卷号	5
发表状态	已发表
DOI	10.1109/ACCESS.2017.2697978
摘要	Turbo compressed sensing (Turbo-CS) is an efficient iterative algorithm for sparse signal recovery with partial orthogonal sensing matrices. In this paper, we extend the Turbo-CS algorithm to solve compressed sensing problems involving a more general signal structure, including compressive image recovery and low-rank matrix recovery. A main difficulty for such an extension is that the original Turbo-CS algorithm requires a prior knowledge of the signal distribution that is usually unavailable in practice. To overcome this difficulty, we propose to redesign the Turbo-CS algorithm by employing a generic denoiser that does not depend on the prior distribution, and hence the name denoising-based Turbo-CS (D-Turbo-CS). We then derive the extrinsic information for a generic denoiser by following the Turbo-CS principle. Based on that, we optimize the parametric extrinsic denoisers to minimize the output mean-square error (MSE). Explicit expressions are derived for the extrinsic SURE-LET denoiser used in image denoising and also for the singular value thresholding denoiser used in low-rank matrix denoising. We find that the dynamics of D-Turbo-CS can be well described by a scaler recursion called MSE evolution, similar to the case for Turbo-CS. Numerical results demonstrate that D-Turbo-CS considerably outperforms the counterpart algorithms in both reconstruction quality and running time.
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收录类别	SCI ; EI
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/2938
专题	信息科学与技术学院信息科学与技术学院_PI研究组_袁晓军组信息科学与技术学院_博士生
作者单位	1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 2.Department of Statistics, Columbia University, New York, NY, USA 3.National Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Zhipeng Xue,Junjie Ma,Xiaojun Yuan. Denoising-Based Turbo Compressed Sensing[J]. IEEE ACCESS,2017,5.
APA	Zhipeng Xue,Junjie Ma,&Xiaojun Yuan.(2017).Denoising-Based Turbo Compressed Sensing.IEEE ACCESS,5.
MLA	Zhipeng Xue,et al."Denoising-Based Turbo Compressed Sensing".IEEE ACCESS 5(2017).