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Gradient projection for linearly constrained convex optimization in sparse signal recovery

8 years 10 months ago
Gradient projection for linearly constrained convex optimization in sparse signal recovery
The 2- 1 compressed sensing minimization problem can be solved efficiently by gradient projection. In imaging applications, the signal of interest corresponds to nonnegative pixel intensities; thus, with additional nonnegativity constraints on the reconstruction, the resulting constrained minimization problem becomes more challenging to solve. In this paper, we propose a gradient projection approach for sparse signal recovery where the reconstruction is subject to nonnegativity constraints. Numerical results are presented to demonstrate the effectiveness of this approach.
Zachary T. Harmany, Daniel Thompson, Rebecca Wille
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICIP
Authors Zachary T. Harmany, Daniel Thompson, Rebecca Willett, Roummel F. Marcia
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