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Proceedings Paper

Convex relaxations of spectral sparsity for robust super-resolution and line spectrum estimation
Author(s): Yuejie Chi
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Paper Abstract

We consider recovering the amplitudes and locations of spikes in a point source signal from its low-pass spectrum that may suffer from missing data and arbitrary outliers. We first review and provide a unified view of several recently proposed convex relaxations that characterize and capitalize the spectral sparsity of the point source signal without discretization under the framework of atomic norms. Next we propose a new algorithm when the spikes are known a priori to be positive, motivated by applications such as neural spike sorting and fluorescence microscopy imaging. Numerical experiments are provided to demonstrate the effectiveness of the proposed approach.

Paper Details

Date Published: 24 August 2017
PDF: 8 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 103941G (24 August 2017); doi: 10.1117/12.2270060
Show Author Affiliations
Yuejie Chi, The Ohio State Univ. (United States)


Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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