Accelerated reconstruction of a compressively sampled data stream

Pantelis Sopasakis, Nikolaos Freris, Panagiotis Patrinos

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed: the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. In this paper, we develop a novel Newton-type forwardbackward proximal method to recursively solve the regularized Least-Squares problem (LASSO) online. We establish global convergence of our method as well as a local quadratic convergence rate. Our simulations show a substantial speed-up over the state of the art which may render the proposed method suitable for applications with stringent real-time constraints.

Original languageEnglish (US)
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9780992862657
StatePublished - Nov 28 2016
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: Aug 28 2016Sep 2 2016

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Other24th European Signal Processing Conference, EUSIPCO 2016


  • Compressed sensing
  • Forward backward splitting
  • Operator splitting methods
  • Recursive algorithms

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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