Methods for Quantized Compressed Sensing
For researchers in compressed sensing, this work provides a comparative catalog and two new algorithms that improve reconstruction from quantized measurements.
The paper compares greedy quantized compressed sensing algorithms and introduces two new methods, QCoSaMP and AOP-QIHT, achieving improved reconstruction performance under given bit-depth, sparsity, and noise levels.
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.