Compressed Sensing and Non-Standard Tomography

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• Stefania Petra (University of Heidelberg )
• Thursday 31 May 2018, 12:00-13:00
• Lecture Room C, Watson Building.

If you have a question about this talk, please contact Sergey Sergeev.

Compressed Sensing (CS) is a new paradigm of mathematical signal processing, which allows under certain assumptions, the recovery of corresponding signal classes from measured values below the classical sampling rate (Nyquist-Shannon Theorem). The extension of the mathematical theory as well as the development of new applications in various fields are the subject of many research activities in CS. The talk presents this topic based on a non-standard problem in tomography, that is equally relevant to theoretical issues of CS and highly relevant for industrial applications.

This talk is part of the Optimisation and Numerical Analysis Seminars series.

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• Lecture Room C, Watson Building
• Optimisation and Numerical Analysis Seminars
• School of Mathematics events

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