Iterative Recovery of Dense Signals from Incomplete Measurements

Norbert Goertz*, Chunli Guo, Alexander Jung, Mike E. Davies, Gerhard Doblinger

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Within the framework of compressed sensing, we consider dense signals, which contain both discrete as well as continuous-amplitude components. We demonstrate by a comprehensive numerical study-to the best of our knowledge the first of its kind in the literature-that dense signals can be recovered from noisy, incomplete linear measurements by simple iterative algorithms that are inspired by or are implementations of approximate message passing. Those iterative algorithms are shown to significantly outperform all other algorithms presented so far, when they use a novel noise-adaptive thresholding function that is proposed in this contribution.

Original languageEnglish
Pages (from-to)1059-1063
Number of pages5
JournalIEEE Signal Processing Letters
Issue number9
Publication statusPublished - Sep 2014


  • Approximate message passing
  • compressed sensing
  • dense signals
  • iterative recovery


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