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The University of Southampton
Computationally Intensive Imaging

Dr Thomas Blumensath BSc, PhD

Associate Professor

Dr Thomas Blumensath's photo

Dr Thomas Blumensath is Associate Professor within Engineering and Physical Sciences at the University of Southampton.

I develop advanced mathematical methods to improve the acquisition and analysis of signals and images. Applications of these techniques include the study of connections in the human brain, the improvement of speech signals and the measurement of the dimensions of manufactured components using x-rays.

Current position

Thomas Blumensath is a Associate Professor at the University of Southampton. He is co-chair of the Computationally Intensive Imaging University Strategic Research Group and Deputy Director of the μ-VIS x-ray imaging centre.

Career history

Thomas Blumensath received a B.Sc. (Hons) in Music Technology and Audio System Design from the University in Derby in 2002 and, in 2006, a PhD in Electronic Engineering (Bayesian Signal Processing) from the University of London.

Since 2005, he held various appointments as Postdoctoral Researcher and Research Fellow working at the Centre for Digital Music at Queen Mary University of London, the Institute for Digital Communications at the University of Edinburgh, the Applied Mathematics Research Group at the University of Southampton and the University of Oxford's Centre for functional MRI of the brain.

In 2012 he joined the Institute of Sound and Vibration Research as a New Frontiers Fellow and now holds a Lectureship in Signal and Image processing.

Research interests

Thomas Blumensath is an engineer and mathematician working on fundamental signal and image processing techniques and their application to a range of scientific problems. Based on a sound mathematical basis, his research aims at the development of advanced methods in signal and image processing and their application to challenging problems in the physical and life sciences. He is particularly interested in x-ray image reconstruction, biomedical applications and human brain mapping.

Specific research interests include:

  • Sparse methods in signal and image processing
  • Geometric aspects of signal and image structure
  • Compressed Sensing
  • Bayesian and non-linear signal processing
  • Human brain mapping and connectonomics
  • Image reconstruction in computed tomography
  • Application of x-ray computed tomography to dimensional metrology
Understanding the human brain
Understanding the human brain
Uncertainties in x-ray tomography
Uncertainties in x-ray tomography
Data geometry
Data geometry
Efficient Computational Algorithms
Efficient Computational Algorithms

Research group

Signal Processing, Audio and Hearing Group

Affiliate research groups

Computationally Intensive Imaging, a University Strategic Research Group, μ-VIS, the University of Southampton’s centre for computed tomography , Institute for Life Sciences

Research project(s)

Reading between the lines: Signal Processing for faster fMRI acquisition

The network in your head: discovering data highways in the human brain

When greed is good: greedy algorithms for Signal Processing and Compressed Sensing

Size matters: x-ray computed tomography for dimensional metrology

Filling in the gaps: compressed sensing for x-ray computed tomography

Fast,large scale optimization algorithms for tomographic image reconstruction

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Book Chapters

  • Blumensath, T. (2014). The geometry of compressed sensing. In A. Y. Carmi, L. Mihaylova, & S. J. Godsill (Eds.), Compressed Sensing and Sparse Filtering (Signals and Communication Technology). Berlin, DE: Springer.
  • Blumensath, T., Davies, M. E., & Rilling, G. (2012). Greedy algorithms for compressed sensing. In Y. C. Eldar, & G. Kutyniok (Eds.), Compressed Sensing: Theory and Applications (pp. 348-393). Cambridge, GB: Cambridge University Press.




Module title Code Role Programme
Biomedical Applications of Signal and Image Processing ISVR6138 Lecturer Acoustical Engineering, Biomedical Engineering
Mathematics for Engineering & the Environment MATH1054 Academic Lead Acoustical/Ship/Civil Engineering
Systems Design & Computing FEEG2001 Lecturer Acoustical Engineering 
Applied Audio Signal Processing ISVR3071 Co-ordinator Acoustical Engineering
Individual Project FEEG3003 Lecturer School of Engineering BEng/MEng programs
MSc Research Project FEEG6012 Lecturer School of Engineering MSc programs
MSc Project COMP6200 Lecturer Artificial Inteligence, and Data Science
Project Preparation ELEC6211 Lecturer Artificial Inteligence, and Data Science

Sparsify Version 0.5

sparsify is a set of Matlab m-files implementing a range of different algorithms to calculate sparse signal approximations. Currently sparsify contains two main sets of algorithms, greedy methods (collected under the name of GreedLab) and hard thresholding algorithms (collected in HardLab). See ALGORITHMS below for a list of available algorithms.

Dr Thomas Blumensath
Building 27, Room 2029, University of Southampton Highfield Southampton SO17 1BJ

Room Number: 13/3063

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