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
Year of Publication2012

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
Citation Keyau-yeung2012SINBADcs