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SUMMARY:Minimisation of sparse higher-order energies for large-scale probl
 ems in imaging - Carola-Bibiane Schönlieb (University of Cambridge)
DTSTART:20110323T160000Z
DTEND:20110323T170000Z
UID:TALK29560@talks.cam.ac.uk
CONTACT:6743
DESCRIPTION:In this talk we discuss the numerical solution of minimisation
  problems promoting higher-order sparsity properties. In particular\, we a
 re interested in total variation minimisation\, which enforces sparsity on
  the gradient of the solution. There are several methods presented in the 
 literature for performing very efficiently total variation minimisation\, 
 e.g.\, for image processing problems of small or medium size. Because of t
 heir iterative-sequential formulation\, none of them is able to address in
  real-time extremely large problems\, such as 4D imaging (spatial plus tem
 poral dimensions) for functional magnetic-resonance in nuclear medical ima
 ging\, astronomical imaging or global terrestrial seismic tomography. For 
 these cases\, we propose subspace splitting techniques\, which accelerate 
 the numerics by dimension reduction and preconditioning. A careful analysi
 s of these algorithms is furnished with a presentation of their applicatio
 n to some imaging tasks. 
LOCATION:Seminar Room 1\, Isaac Newton Institute for Mathematical Sciences
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