Title: Reweighted L1 Minimization for Compressed Sensing
Abstract:Recent work in compressed sensing theory shows that m×n independent and identically distributed sensing matrices whose entries are drawn independently from certain probability distributions guarantee ...Recent work in compressed sensing theory shows that m×n independent and identically distributed sensing matrices whose entries are drawn independently from certain probability distributions guarantee exact recovery of a sparse signal with high probability even if m≪n. In particular, it is well understood that the L1 minimization algorithm is able to recover sparse signals from incomplete measurements. In this paper, we propose a novel sparse signal reconstruction method that is based on the reweighted L1 minimization via support recovery.Read More
Publication Year: 2010
Publication Date: 2010-07-01
Language: en
Type: article
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Cited By Count: 3
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