Compressed sensing, recovery of signals using random Turbo matrices

TitleCompressed sensing, recovery of signals using random {Turbo} matrices
Publication TypeSINBAD Presentation
AuthorsEnrico Au-Yeung, Ozgur Yilmaz, Felix J. Herrmann
PublisherSINBAD
Year of Publication2013
Abstract

Compressed sensing is an emerging technique that allows us to recover an image using far fewer number of measurements than classical sampling techniques. Designing the measurement matrices with certain properties are critical to this task. Gaussian matrices are most commonly used. We discover a new class of random matrices that can outperform the Gaussian matrices when we are in a situation of taking an outrageously small number of samples.

KeywordsPresentation, private, SINBAD, SINBADFALL2013, SLIM
URLhttps://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2013/Fall/yeung2013SINBADcsr/yeung2013SINBADcsr.pdf
Citation Keyyeung2013SINBADcsr