5 accepted papers
In this paper, we propose new gradient-based methods for image reconstruction from partial Fourier measurements, which are commonly used in magnetic resonance imaging (MRI) or synthetic aperture radar. Compared to classical gradient recovery methods, a key improvement is obtained by formulating the…
In this paper, we propose a new method for the recovery of a sparse signal from few linear measurements using a reference signal as side information. Modeling the signal coefficients with a double Laplace mixture model, and assuming that the distribution of the components of the prior information di…
Iterative shrinkage-thresholding algorithms provide simple methods to recover sparse signals from compressed measurements. In this paper, we propose a new class of iterative shrinkage-thresholding algorithms which preserve the computational simplicity and improve iterative estimation by incorporatin…
In this paper, we propose a method for estimating the sparsity of a signal from its noisy linear projections without recovering it. The method exploits the property that linear projections acquired using a sparse sensing matrix are distributed according to a mixture distribution whose parameters dep…
In this paper, we analyze a new class of iterative re-weighted least squares (IRLS) algorithms and their effectiveness in signal recovery from incomplete and inaccurate linear measurements. These methods can be interpreted as the constrained maximum likelihood estimation under a two-state Gaussian s…