Title: Adaptive reduced-set matching pursuit for compressed sensing recovery
Abstract:Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Various greedy recovery algorithms have been proposed to achieve a lower computational ...Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Various greedy recovery algorithms have been proposed to achieve a lower computational complexity compared to the optimal ℓ1 minimization, while maintaining a good reconstruction accuracy. We propose a new greedy recovery algorithm for compressed sensing, called the Adaptive Reduced-set Matching Pursuit (ARMP). Our algorithm achieves higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to ℓ1 minimization in terms of the normalized time-error product, a metric that we introduced to measure the trade-off between the reconstruction time and error.Read More
Publication Year: 2016
Publication Date: 2016-09-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 14
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