Given a signal sparse in a redundant frame, how to recover it with substantially undersampled linear measurements? The redundant frame component adds complexity to the problem. We will survey current results, list some fundamental problems that need to be solve, and present new results on both deterministic and random measurements. We show that subgaussian measurements achieve the minimum number of measurements and these results complement the compressed sensing literature.
14 Jun 2019
3:30pm - 4:30pm
Where
Room 4504, Academic Building, (Lifts 25-26)
Speakers/Performers
Prof. Xuemei Chen
Department of Mathematical Sciences, New Mexico State University
Organizer(S)
Department of Mathematics
Contact/Enquiries
mathseminar@ust.hk
Payment Details
Audience
Alumni, Faculty and Staff, PG Students, UG Students
Language(s)
English
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