# Compressed sensing, recovery of signals using random Turbo matrices

 Title Compressed sensing, recovery of signals using random {Turbo} matrices Publication Type SINBAD Presentation Authors Enrico Au-Yeung, Ozgur Yilmaz, Felix J. Herrmann Publisher SINBAD Year of Publication 2013 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. Keywords Presentation, private, SINBAD, SINBADFALL2013, SLIM URL https://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2013/Fall/yeung2013SINBADcsr/yeung2013SINBADcsr.pdf Citation Key yeung2013SINBADcsr