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Reweighted Block-Based Compressed Sensing Using Singular Value Decomposition

Citation Author(s):
Niloufar Abaei, Seyed Hamid Safavi, Farah Torkamani-Azar
Submitted by:
Seyed Hamid Safavi
Last updated:
14 March 2017 - 5:40am
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Seyed Hamid Safavi
Paper Code:
ICASSP-PMF-P4.4
Categories:
Keywords:
 

Existed inherent sparsity of natural images in some domains helps to reconstruct the signal with a lower number of measurements. To benefit from the sparsity, one should solve the reweighted $\ell_{1}$-norm minimization algorithms. Although, the existed reweighted $\ell_{1}$-norm minimization approaches work well for k-sparse signals, but, the performance of these methods for compressible signals are not competitive with unweighted one. Motivated by this challenge, in this paper, we propose a new reweighted $\ell_{1}$-minimization algorithm based on singular value decomposition (SVD) of compressible signals like images. Moreover, we develop our proposed algorithm on the block-based compressed sensing (BCS) to make it applicable to large-size images. Simulation results also demonstrated the superiority of our proposed method over current state-of-the-art reweighted CS reconstruction algorithms for natural images.

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