Disjunct Matrices for Compressed Sensing
Pradip Sasmal, Sai Subramanyam Thoota, Chandra R. Murthy
Abstract
Disjunct matrices play a central role in non-adaptive group testing, as they provide necessary and sufficient conditions for identifying defective items from a large population using a small number of tests. In this paper, we show that binary disjunct matrices can also be very useful for recovering sparse signals from underdetermined linear measurements. They admit non-iterative, ultra-low complexity recovery of sparse signals. Binary measurement matrices have the added benefit of being friendly for hardware implementation. Further, we generalize the notion of disjunctness to matrices with arbitrary (non-binary) entries and show that such matrices also admit similar fast sparse vector recovery algorithms. We empirically demonstrate that disjunct matrices can recover denser signals than recent non-iterative sparse recovery algorithms.
BibTeX
@inproceedings{icassp2019_disjunctmatrices,
title = {Disjunct Matrices for Compressed Sensing},
author = {Pradip Sasmal and Sai Subramanyam Thoota and Chandra R. Murthy},
booktitle = {ICASSP 2019},
year = {2019}
}