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SUMMARY:Breaking the coherence barrier: asymptotic incoherence and asympto
 tic sparsity in compressed sensing
DTSTART:20130417T110000
DTSTAMP:20260916T201413Z
UID:417c3c7e7bbaf31b800af96f476f0a1c00f83f1a986f4b9a7ebce501
CATEGORIES:Conferences - Seminars
DESCRIPTION:Anders Hansen\, University of Cambridge\nBio: Dr Hansen comple
 ted his PhD at Cambridge and then moved to Caltech as von Karman Fellow\, 
 before returning to Cambridge as a Research Fellow of Homerton College. Hi
 s research is in computational mathematics and includes applied harmonic a
 nalysis\, mathematical signal processing with emphasis on sampling theory 
 and compressed sensing as well as spectral theory\, computability theory\,
  complexity theory and numerical analysis: a rapidly growing area of mathe
 matics with enormous applications ranging from medical imaging to signal p
 rocessing.\nIn this talk we will discuss new results on how to break the s
 o called coherence barrier in compressed sensing. More precisely\, it is w
 ell known that compressed sensing relies on two key pillars: incoherence a
 nd sparsity. Although there are examples where one has both of these featu
 res\, there are surprisingly many cases in applications where these criter
 ia are not satisfied\, in particular Fourier sampling and wavelet or polyn
 omial reconstruction (both essential in Magnetic Resonance Imaging (MRI)).
  This has led to a substantial gap between the state of the art compressed
  sensing theory and its actual use in application. I will show how to brid
 ge this fundamental gap by introducing a new theory where the assumptions 
 of incoherence and sparsity are replaced by two new features: asymptotic i
 ncoherence and asymptotic sparsity as well as multi-level sampling. With t
 hese new tools at hand I will demonstrate two rather intriguing effects: T
 he success of compressed sensing is resolution dependent and optimal sampl
 ing strategies are signal structure dependent.
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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