Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

In this article a unified approach to iterative soft-thresholding algorithms for the solution of
linear operator equations in infinite dimensional Hilbert spaces is presented. We formulate …

Mixed norms are used to exploit in an easy way, both structure and sparsity in the framework
of regression problems, and introduce implicitly couplings between regression coefficients …

Traditional sparse image models treat color image pixel as a scalar, which represents color
channels separately or concatenate color channels as a monochrome image. In this paper …

The split feasibility problem (SFP) consists of finding a common point in the intersection of
finitely many convex sets, where some of the sets arise by imposing convex constraints in …

Sparse regression often uses ℓ p norm priors (with p< 2). This paper demonstrates that the
introduction of mixed-norms in such contexts allows one to go one step beyond in signal …

In this paper, we deal with the solution of linear and non-linear geophysical ill-posed
problems by requiring the solution to have sparse representations in two appropriate …

The quaternion method plays an important role in color image processing, because it
represents the color image as a whole rather than as a separate color space component …

This paper deals with a theoretical analysis of a novel regularization technique for
(nonlinear) inverse problems, in the field of the so-called sparsity promoting regularizations …

The goal of this paper is the formulation of an abstract setting that can be used for the
derivation of linear convergence rates for a large class of sparsity promoting regularization …