Methods for Quantized Compressed Sensing

TL;DR

This paper evaluates and compares different greedy algorithms for quantized compressed sensing, introduces two novel methods, and analyzes their performance in reconstructing sparse signals from quantized measurements.

Contribution

It introduces two new greedy algorithms, QCoSaMP and AOP-QIHT, for improved sparse signal reconstruction in quantized compressed sensing.

Findings

Performance varies with bit-depth, sparsity, and noise level.

New algorithms outperform some existing methods under certain conditions.

Comprehensive comparison guides future algorithm development.

Abstract

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.