Super-Resolution by Compressive Sensing Algorithms
Abstract
In this work, super-resolution by 4 compressive sensing methods (OMP, BP, BLOOMP, BP-BLOT) with highly coherent partial Fourier measurements is comparatively studied. An alternative metric more suitable for gauging the quality of spike recovery is introduced and based on the concept of filtration with a parameter representing the level of tolerance for support offset. In terms of the filtered error norm only BLOOMP and BP-BLOT can perform grid-independent recovery of well separated spikes of Rayleigh index 1 for arbitrarily large super-resolution factor. Moreover both BLOOMP and BP-BLOT can localize spike support within a few percent of the Rayleigh length. This is a weak form of super-resolution. Only BP-BLOT can achieve this feat for closely spaced spikes separated by a fraction of the Rayleigh length, a strong form of super-resolution.
Cite
@article{arxiv.1211.5870,
title = {Super-Resolution by Compressive Sensing Algorithms},
author = {A. Fannjiang and W. Liao},
journal= {arXiv preprint arXiv:1211.5870},
year = {2012}
}
Comments
IEEE Proceeding Asilomar conference on signals, systems and computers. Nov. 4-7, 2012