Spectral Compressive Sensing with Model Selection
Information Theory
2014-07-15 v3 math.IT
Abstract
The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and the discretization of frequency parameter space. In this paper, we adopt a parametric joint recovery-estimation method based on model selection in spectral compressive sensing. Numerical experiments show that our approach outperforms most state-of-the-art spectral CS recovery approaches in fidelity, tolerance to noise and computation efficiency.
Cite
@article{arxiv.1311.6916,
title = {Spectral Compressive Sensing with Model Selection},
author = {Zhenqi Lu and Rendong Ying and Sumxin Jiang and Zenghui Zhang and Peilin Liu and Wenxian Yu},
journal= {arXiv preprint arXiv:1311.6916},
year = {2014}
}
Comments
5 pages, 2 figures, 1 table, published in ICASSP 2014