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Cite Details

Rick Chartrand, "Nonconvex compressive sensing and reconstruction of gradient-sparse images: random vs. tomographic Fourier sampling", in IEEE International Conference on Image Processing (ICIP), 2008

Abstract

Previous compressive sensing papers have considered the example of recovering an image with sparse gradient from a surprisingly small number of samples of its Fourier transform. The samples were taken along radial lines, this being equivalent to a tomographic reconstruction problem. The theory of compressive sensing, however, considers random sampling instead. We perform numerical experiments to compare the two approaches, in terms of the number of samples necessary for exact recovery, algorithmic performance, and robustness to noise. We use a nonconvex approach, this having previously been shown to allow reconstruction with fewer measurements and greater robustness to noise, as confirmed by our results here.

BibTeX Entry

@inproceedings{chartrand-2008-nonconvex,
author = {Rick Chartrand},
title = {Nonconvex compressive sensing and reconstruction of gradient-sparse images: random vs. tomographic Fourier sampling},
year = {2008},
urlpdf = {http://math.lanl.gov/Research/Publications/Docs/chartrand-2008-nonconvex.pdf},
booktitle = {IEEE International Conference on Image Processing (ICIP)}
}