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  • Emmanuel J. Candès, Justin K. Romberg, Terence Tao
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • 2006 (First Publication: 10 September 2004)
  • This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f/spl isin/C/sup N/ and a randomly chosen set of frequencies /splExpand
  • J. Romberg
  • Mathematics, Computer Science
  • SIAM J. Imaging Sci.
  • 2009 (First Publication: 1 October 2009)
  • This paper demonstrates that convolution with random waveform followed by random time-domain subsampling is a universally efficient compressive sensing strategy. We show that an $n$-dimensionalExpand
  • E. Candès, J. Romberg
  • Mathematics, Computer Science, Engineering
  • IS&T/SPIE Electronic Imaging
  • 2005 (First Publication: 11 March 2005)
  • Can we recover a signal f∈RN from a small number of linear measurements? A series of recent papers developed a collection of results showing that it is surprisingly possible to reconstruct certainExpand