Binary Compressive Sensing via Smoothed $\ell_0$ Gradient Descent
Signal Processing
2018-07-31 v2 Machine Learning
Abstract
We present a Compressive Sensing algorithm for reconstructing binary signals from its linear measurements. The proposed algorithm minimizes a non-convex cost function expressed as a weighted sum of smoothed norms which takes into account the binariness of signals. We show that for binary signals the proposed algorithm outperforms other existing algorithms in recovery rate while requiring a short run time.
Keywords
Cite
@article{arxiv.1801.09937,
title = {Binary Compressive Sensing via Smoothed $\ell_0$ Gradient Descent},
author = {Tianlin Liu and Dae Gwan Lee},
journal= {arXiv preprint arXiv:1801.09937},
year = {2018}
}