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Research project

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

Staff

Lead researchers

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
Connect with Thomas

Email: Thomas.Blumensath@soton.ac.uk

Research outputs

Directional clustering through matrix factorization
Thomas Blumensath, 2016, IEEE Transactions on Neural Networks and Learning Systems, 27(10), 2095-2107
DOI: 10.1109/TNNLS.2015.2505060
Type: article
k-t FASTER: a novel method for accelerating FMRI data acquisition using low rank constraints
Mark Chiew, Steve M. Smith, Peter J. Koopmans, Nadine N. Graedel, Thomas Blumensath & Karla L. Miller, 2015, Magnetic Resonance in Medicine, 74(2), 353-364
DOI: 10.1002/mrm.25395
Type: article
Non-convexly constrained image reconstruction from nonlinear tomographic X-ray measurements
Thomas Blumensath & Richard P. Boardman, 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2043)
DOI: 10.1098/rsta.2014.0393
Type: article
A spatially constrained low-rank matrix factorization for the functional parcellation of the brain
A. Benichoux & T. Blumensath, 2014, Proc. 22nd European Signal Processing Conference, 1-5
Type: article
Non-linear compressed sensing and its application to beam hardening correction in x-ray tomography
T. Blumensath, 2014
Type: other
Monte Carlo methods for compressed sensing
Thomas Blumensath, 2014
DOI: 10.1109/ICASSP.2014.6853747
Type: conference
Sparse matrix decompositions for clustering
Thomas Blumensath, 2014
Type: conference