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SUMMARY:Compressive sensing principles and iterative sparse recovery for i
 nverse and ill-posed problems - Gerd Teschke (TU Braunschweig)
DTSTART:20101109T150000Z
DTEND:20101109T160000Z
UID:TALK27600@talks.cam.ac.uk
CONTACT:Dr Shadrin
DESCRIPTION:In this talk we shall be concerned with compressive sampling s
 trategies and sparse recovery principles for linear inverse and ill-posed 
 problems. As the main result\, we provide compressed measurement models fo
 r ill-posed problems and recovery accuracy\nestimates for sparse approxima
 tions of the solution of the underlying inverse problem. The main ingredie
 nts are variational formulations that allow the treatment of ill-posed ope
 rator equations in the context of compressively sampled data. In particula
 r\, we rely on Tikhonov variational and constrained optimization formulati
 ons. One essential difference to the classical compressed sensing framewor
 k is the incorporation of joint\nsparsity measures allowing the treatment 
 of infinite dimensional\nreconstruction spaces. The theoretical results ar
 e furnished with a number of numerical experiments.
LOCATION:CMS\, MR14
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