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ICASSP
2009
IEEE

A simple, efficient and near optimal algorithm for compressed sensing

10 years 4 months ago
A simple, efficient and near optimal algorithm for compressed sensing
When sampling signals below the Nyquist rate, efficient and accurate reconstruction is nevertheless possible, whenever the sampling system is well behaved and the signal is well approximated by a sparse vector. This statement has been formalised in the recently developed theory of compressed sensing, which developed conditions on the sampling system and proved the performance of several efficient algorithms for signal reconstruction under these conditions. In this paper, we prove that a very simple and efficient algorithm, known as Iterative Hard Thresholding, has near optimal performance guarantees rivalling those derived for other state of the art approaches.
Thomas Blumensath, Mike E. Davies
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Thomas Blumensath, Mike E. Davies
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