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SUMMARY:Compressed sensing for the sparse Radon transform - Giovanni Alber
 ti (University of Genova)
DTSTART:20240216T140000Z
DTEND:20240216T150000Z
UID:TALK209551@talks.cam.ac.uk
CONTACT:Dr Sergio Bacallado
DESCRIPTION:Compressed sensing allows for the recovery of sparse signals f
 rom a limited number of measurements\, which is proportional - up to logar
 ithmic factors - to the sparsity of the unknown signal. The classical theo
 ry mostly considers either random linear measurements or subsampled isomet
 ries. In particular\, the case with the subsampled Fourier transform finds
  applications to undersampled magnetic resonance imaging. In this talk\, I
  will show how the theory of compressed sensing can also be rigorously app
 lied to the sparse Radon transform\, in which only a finite number of angl
 es are considered. One of the main novelties consists in the fact that the
  Radon transform is associated to an ill-posed inverse problem\, and the r
 esult follows from a new theory of compressed sensing for abstract inverse
  problems. 
LOCATION:MR12\, Centre for Mathematical Sciences
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