Generalized sampling and infinite-dimensional compressed sensing

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• Hansen, A (Humboldt)
• Thursday 24 March 2011, 16:00-17:00
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact Mustapha Amrani.

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We will discuss a generalization of the Shannon Sampling Theorem that allows for reconstruction of signals in arbitrary bases. Not only can one reconstruct in arbitrary bases, but this can also be done in a completely stable way. When extra information is available, such as sparsity or compressibility of the signal in a particular bases, one may reduce the number of samples dramatically. This is done via Compressed Sensing techniques, however, the usual finite-dimensional framework is not sufficient. To overcome this obstacle I’ll introduce the concept of Infinite-Dimensional Compressed Sensing.

This talk is part of the Isaac Newton Institute Seminar Series series.

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