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University of Cambridge > Talks.cam > LMS Invited Lectures 2011 > Generalized sampling and infinite-dimensional compressed sensing
Generalized sampling and infinite-dimensional compressed sensingAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact ai10. 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 LMS Invited Lectures 2011 series. This talk is included in these lists:
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