Within the framework of the \ell_0 regularized least squares problem, we focus, in this paper,
on nonconvex continuous penalties approximating the \ell_0-norm. Such penalties are …

We have an M*N real-valued arbitrary matrix A (eg, a dictionary) with M<N and data d
describing the sought-after object with the help of A. This work provides an in-depth analysis …

We develop a primal dual active set with continuation algorithm for solving the ℓ 0-
regularized least-squares problem that frequently arises in compressed sensing. The …

Two widely used models to find a sparse solution from a noisy underdetermined linear
system are the constrained problem where the quadratic error is minimized subject to a …

Energy minimization methods are a very popular tool in image and signal processing. This
chapter deals with images defined on a discrete finite set. Energy minimization methods are …

Motivated by variational models in continuum mechanics, we introduce a novel algorithm to
perform nonsmooth and nonconvex minimizations with linear constraints in Euclidean …

Simulated annealing (SA) is a generic optimization method that is quite popular because of
its ease of implementation and its global convergence properties. However, SA is widely …

This paper proposes an edge-preserving regularization technique to solve the color image
demosaicing problem in the realistic case of noisy data. We enforce intra-channel local …

Simulated annealing (SA) is a generic optimization method whose popularity stems from its
simplicity and its global convergence properties; it emulates the physical process of …

The problem of image restoration from blur and noise is studied. By regularization
techniques, a solution of the problem is found as the minimum of a primal energy function …