Claremont McKenna College, Mathematics (CMC)
In this paper, we compare and catalog the performance of various greedy quantized compressed sensing algorithms that reconstruct sparse signals from quantized compressed measurements. We also introduce two new greedy approaches for reconstruction: Quantized Compressed Sampling Matching Pursuit (QCoSaMP) and Adaptive Outlier Pursuit for Quantized Iterative Hard Thresholding (AOP-QIHT). We compare the performance of greedy quantized compressed sensing algorithms for a given bit-depth, sparsity, and noise level.
© 2016 Shi, Case, Gu, Tu, Needell
H. M. Shi, M. Case, X. Gu, S. Tu, and D. Needell. “Methods for Quantized Compressed Sensing.” Proc. Information Theory and Applications (ITA), La Jolla CA, Jan. 2016.