This paper explores a novel setting for compressed sensing (CS) in which the sampling trajectory length is a critical bottleneck and must be minimized subject to constraints on th...
This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gau...
Nicolas Dobigeon, Alfred O. Hero, Jean-Yves Tourne...
We investigate the recovery of signals exhibiting a sparse representation in a general (i.e., possibly redundant or incomplete) dictionary that are corrupted by additive noise adm...
Christoph Studer, Patrick Kuppinger, Graeme Pope, ...
Abstract--Digital processing techniques are based on representing a continuous-time signal by a discrete set of samples. This paper treats the problem of reconstructing a periodic ...
—This paper considers the reconstruction of structured-sparse signals from noisy linear observations. In particular, the support of the signal coefficients is parameterized by h...