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arXiv · 1311.6916

Spectral Compressive Sensing with Model Selection

Zhenqi Lu·Rendong Ying·Sumxin Jiang·Zenghui Zhang·Peilin Liu·Wenxian Yu
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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.

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Zhenqi Lu, Rendong Ying, Sumxin Jiang, Zenghui Zhang, Peilin Liu, Wenxian Yu. 2014-07-12. Spectral Compressive Sensing with Model Selection. https://arxiv.org/abs/1311.6916

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