We consider the reconstruction of images by minimizing regularized cost-functions. To accelerate the computation of the estimate, two forms of half-quadratic regularization, multiplicative and additive, are often used. The goal of this paper is to compare both theoretically and experimentally the efficiency of these two forms. We provide a theoretical and experimental analysis of the speed of convergence that they allow. We show that the multiplicative form gives rise to a better rate of convergence.published_or_final_versio
In this paper, we consider the `p-`q minimization problem with 0 < p, q ≤ 2. The problem has been...
Abstract. We consider numerical methods for solving problems involving total variation (TV) regulari...
Computer vision requires the solution of many ill-posed problems such as optical flow, structure fro...
Abstract. We address the minimization of regularized convex cost functions which are cus-tomarily us...
We consider the reconstruction of MRI images by minimizing regularized cost-functions. To accelerate...
Abstract—A popular way to restore images comprising edges is to minimize a cost function combining a...
The article addresses a wide class of image deconvolution or reconstruction situations where a sough...
Abstract. We address the minimization of penalized least squares (PLS) criteria customarily used for...
International audienceWe study the global and local convergence of a generic half-quadratic optimiza...
We present a new mixed regularization method for image recovery. The method is based on the combinat...
One popular method for the recovery of an ideal intensity image from corrupted or indirect measureme...
International audienceWe present new global convergence results for half-quadratic optimization in t...
Image restoration is often solved by minimizing an energy function consisting of a data-fidelity ter...
AbstractThe equivalence of minimum-norm least-squares solutions of systems of linear equations and s...
Abstract. We present a new mixed regularization method for image recovery. The method is based on th...
In this paper, we consider the `p-`q minimization problem with 0 < p, q ≤ 2. The problem has been...
Abstract. We consider numerical methods for solving problems involving total variation (TV) regulari...
Computer vision requires the solution of many ill-posed problems such as optical flow, structure fro...
Abstract. We address the minimization of regularized convex cost functions which are cus-tomarily us...
We consider the reconstruction of MRI images by minimizing regularized cost-functions. To accelerate...
Abstract—A popular way to restore images comprising edges is to minimize a cost function combining a...
The article addresses a wide class of image deconvolution or reconstruction situations where a sough...
Abstract. We address the minimization of penalized least squares (PLS) criteria customarily used for...
International audienceWe study the global and local convergence of a generic half-quadratic optimiza...
We present a new mixed regularization method for image recovery. The method is based on the combinat...
One popular method for the recovery of an ideal intensity image from corrupted or indirect measureme...
International audienceWe present new global convergence results for half-quadratic optimization in t...
Image restoration is often solved by minimizing an energy function consisting of a data-fidelity ter...
AbstractThe equivalence of minimum-norm least-squares solutions of systems of linear equations and s...
Abstract. We present a new mixed regularization method for image recovery. The method is based on th...
In this paper, we consider the `p-`q minimization problem with 0 < p, q ≤ 2. The problem has been...
Abstract. We consider numerical methods for solving problems involving total variation (TV) regulari...
Computer vision requires the solution of many ill-posed problems such as optical flow, structure fro...