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Nonconvex compressive sensing and reconstruction of gradient-sparse images: Random vs. tomographic Fourier sampling

9 years 12 months ago
Nonconvex compressive sensing and reconstruction of gradient-sparse images: Random vs. tomographic Fourier sampling
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.
Rick Chartrand
Added 20 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2008
Where ICIP
Authors Rick Chartrand
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