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Research project

Blumensath (first grant)-Constrained low rank matrix recovery:from efficient algorithms to brain network imaging

Staff

Lead researcher

Professor Thomas Blumensath

Professor

Research interests

  • I develop and study advanced algorithms that can solve challenging inverse problems by efficiently exploiting complex prior information. Using techniques from mathematics, statistics and machine learning, my work concentrates primarily on problems in x-ray tomographic image reconstruction and modelling.
  • I work closely with state-of-the-art imaging facilities (µ-VIS, the National Research Facility in Lab-based XCT, the UK’s synchrotron facility at the Diamond Light Source, and ISIS neutron imaging beamline) to find practical solutions to a range of important scientific problems from plant science to manufacturing.
  • My research interests cover areas such as: Theoretical and computational methods for Signal and Image Processing (Machine Learning, Compressed Sensing, Statistical Signal and Image Processing, Quantum Computing, Inverse Problems, Optimisation, X-ray Tomographic Imaging); Advanced tomographic imaging strategies: (limited angle tomography and laminography, Spectral X-ray imaging, Stereo and extreme limited view tomography); Efficient computational methods for tomographic reconstruction, including GPU acceleration, distributed computation and advanced optimisation strategies, Constrained optimisation for ill-conditioned and underdetermined tomographic inverse problems, Applications of X-ray tomography to the inspection of manufactured components, Multimodal tomographic imaging

Research outputs

, 2016 , IEEE Transactions on Neural Networks and Learning Systems , 27 (10) , 2095--2107
Type: article
Mark Chiew,
Steve M. Smith,
Peter J. Koopmans,
Nadine N. Graedel,
& Karla L. Miller
, 2015 , Magnetic Resonance in Medicine , 74 (2) , 353--364
Type: article
A. Benichoux,
, 2014 , Proc. 22nd European Signal Processing Conference , 1--5
Type: article
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