Recovering signals with variable sparsity levels from the noisy 1-bit compressive measurements
Paper in proceedings, 2014

In this paper, we consider the 1-bit compressive sensing reconstruction problem in a scenario that the sparsity level of the signal is unknown and time variant, and the binary measurements are contaminated with the noise. We introduce a new reconstruction algorithm which we refer to as Noise-Adaptive Restricted Step Shrinkage (NARSS). NARSS is superior in terms of performance, complexity and speed of convergence to the algorithms already introduced in the literature for 1-bit compressive sensing reconstruction from the noisy binary measurements.

one bit quantization

compressive sensing (CS)


A. Movahed

University of New South Wales (UNSW)

Ashkan Panahi

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Mark C. Reed

University of New South Wales (UNSW)

2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014; Florence; Italy; 4 May 2014 through 9 May 2014

1520-6149 (ISSN)


Subject Categories

Signal Processing





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