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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.

Inverse Problems

I will discuss a generalization of the Shannon Sampling Theorem that allows for reconstruction of signals in arbitrary bases (and frames). 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 basis, 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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