Compressed sensing, random Fourier matrix and jitter sampling

TitleCompressed sensing, random {Fourier} matrix and jitter sampling
Publication TypeSINBAD Presentation
AuthorsEnrico Au-Yeung, Hassan Mansour, Ozgur Yilmaz
PublisherSINBAD
Year of Publication2012
Abstract

Compressed sensing is an emerging signal processing technique that allows signals to be sampled well below the Nyquist rate, when the signal has a sparse representation in an orthonormal basis. By using a random Fourier matrix or a Gaussian matrix as our measurement matrix, we can reconstruct a signal from far fewer measurements than required by Shannon sampling theorem. In this talk, we will discuss the role of uniform versus jitter sampling, both in a theoretical and practical viewpoint.

KeywordsPresentation, SINBAD, SINBADFALL2012, SLIM
URLhttps://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2012/Fall/au-yeung2012SINBADcs/au-yeung2012SINBADcs_pres.pdf
Citation Keyau-yeung2012SINBADcs