Paper Info
Title
Denoising-Based Turbo Compressed Sensing.
Abstract
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 TurboCS 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.
Year
DOI
Venue
2017
10.1109/ACCESS.2017.2697978
IEEE ACCESS
Keywords
DocType
Volume
Compressed sensing,message passing,orthogonal sensing matrix,denoising,MSE evolution
Journal
5
ISSN
Citations 
PageRank 
2169-3536
2
0.37
References 
Authors
14
3
Name
Order
Citations
PageRank
Zhipeng Xue141.76
Junjie Ma214815.24
Xiaojun Yuan310610.91