Compressive Sensing Using Greedy Basis Pursuit
Date Issued
2008
Date
2008
Author(s)
Liang, Long-Wei
Abstract
In the last few years, people compress signals after acquiring them. In the process of compression, there would be some information discarded from the signal by the compression algorithm. It is a waste that one obtains a signal and then throws parts of them away. If the compression ratio is large, it means one spend unnecessary time on acquiring this signal. Now we introduce a novel method that acquires and compresses a signal simultaneously, called Compressive Sensing. After compressive sensing a signal, one can get a condensed measurement. The minimization of l 1-norm is used to recover the signal from the measurement. Many algorithms can handle this problem, such as Matching Pursuit, Basis Pursuit and so on. Now we apply a faster algorithm to the problem, that is, Greedy Basis Pursuit. By the CS theory, one acquires a signal in a condensed form. Hence this theory beats the Shannon sampling theorem because it samples signals at a rate significantly below the Nyquist rate.
Subjects
Compressive Sensing
Greedy Basis Pursuit
Type
thesis
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