Super-resolution via Superset Selection and Pruning

Authors

Laurent Demanet, Massachusetts Institute of Technology
Deanna Needell, Claremont McKenna CollegeFollow
Nam Nguyen, Massachusetts Institute of Technology

Document Type

Conference Proceeding

Department

Mathematics (CMC)

Publication Date

6-10-2013

Abstract

We present a pursuit-like algorithm that we call the "superset method" for recovery of sparse vectors from consecutive Fourier measurements in the super-resolution regime. The algorithm has a subspace identification step that hinges on the translation invariance of the Fourier transform, followed by a removal step to estimate the solution's support. The superset method is always successful in the noiseless regime (unlike L1-minimization) and generalizes to higher dimensions (unlike the matrix pencil method). Relative robustness to noise is demonstrated numerically.

Rights Information

© 2013 SAMPTA

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Recommended Citation

Demanet, L., Needell, D., Nguyen, N., "Super-resolution via superset selection and pruning", Proc. 10th International Conf. on Sampling Theory and Applications (SAMPTA) 2013. http://arxiv.org/abs/1302.6288