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Title: On the reconstruction of sequences of sparse signals - The Weighted-CS
Authors: Zonoobi, D.
Kassim, A.A. 
Keywords: Compressive sampling
Compressive sensing
Medical image reconstruction
Priori knowledge
Probability model
Sequential CS
Time-varying signals
Weighted ℓ1 minimization
Issue Date: Feb-2013
Citation: Zonoobi, D., Kassim, A.A. (2013-02). On the reconstruction of sequences of sparse signals - The Weighted-CS. Journal of Visual Communication and Image Representation 24 (2) : 196-202. ScholarBank@NUS Repository.
Abstract: In this paper, we study the problem of recursively reconstructing time sequences of sparse signals, where sparsity changes smoothly with time. The idea is to use the signal/image of the previous time instance to extract an estimated probability model for the signal/image of interest, and then use this model to guide the reconstruction process. We examine and illustrate the performance of our approach, "Weighted-CS", with both synthetic and real medical signals/images. It is shown that we can achieve significant performance improvement, using fewer number of samples, compared to other state-of-art Compressive Sensing methods. © 2012 Elsevier Inc. All rights reserved.
Source Title: Journal of Visual Communication and Image Representation
ISSN: 10473203
DOI: 10.1016/j.jvcir.2012.05.002
Appears in Collections:Staff Publications

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