Compressed sensing, random Fourier matrix and jitter sampling
Title | Compressed sensing, random Fourier matrix and jitter sampling |
Publication Type | Presentation |
Year of Publication | 2012 |
Authors | Enrico Au-Yeung, Hassan Mansour, Ozgur Yilmaz |
Keywords | Presentation, SINBAD, SINBADFALL2012, SLIM |
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. |
URL | https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/au-yeung2012SINBADcs/au-yeung2012SINBADcs_pres.pdf |
Citation Key | au-yeung2012SINBADcs |