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Generalized sampling and infinite-dimensional compressed sensing

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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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Hybrid: Seminar room, SPRI & Zoom - email organiser for details Average on weekdays: 8936.85159817 kWh Average on Saturdays: 7838.89506173 kWh

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