AbstractThe equivalence of minimum-norm least-squares solutions of systems of linear equations and standard iterative methods of solution is well established. On the other hand, while it is generally understood that truncated iteration is a form of regularization, comparatively few papers have formalized the relationship between direct methods of regularization and truncated iteration. A brief review of such papers is presented. The main result of this paper is to carry this idea one step further and prove that solutions by direct regularization are in fact identical to solutions of a certain type of truncated-iterative method, and conversely. This equivalence is proved by construction for a very general form of regularization method in whi...
Regularization methods are a key tool in the solution of inverse problems. They are used to introduc...
Many computer vision problems are formulated as an objective function consisting of a sum of functio...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
AbstractThe equivalence of minimum-norm least-squares solutions of systems of linear equations and s...
We prove that solutions by direct regularization of linear systems are equivalent to truncated itera...
AbstractWe prove that solutions by direct regularization of linear systems are equivalent to truncat...
none4noMany real-world applications are addressed through a linear least-squares problem formulatio...
AbstractWe present a unifying framework for a wide class of iterative methods in numerical linear al...
Several iterative methods are available for solving the ill-posed problem of image reconstruction. T...
Abstract. This paper is concerned with the computation of accurate approximate solutions of linear s...
Abstract. In many inverse problems it is essential to use regularization methods that preserve edges...
The Tikhonov-Phillips method is widely used for regularizing ill-posed problems due to the simplicit...
We consider the reconstruction of images by minimizing regularized cost-functions. To accelerate the...
This paper studies the regularization of constrained Maximum Likelihood iterative algorithms applied...
. Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditio...
Regularization methods are a key tool in the solution of inverse problems. They are used to introduc...
Many computer vision problems are formulated as an objective function consisting of a sum of functio...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
AbstractThe equivalence of minimum-norm least-squares solutions of systems of linear equations and s...
We prove that solutions by direct regularization of linear systems are equivalent to truncated itera...
AbstractWe prove that solutions by direct regularization of linear systems are equivalent to truncat...
none4noMany real-world applications are addressed through a linear least-squares problem formulatio...
AbstractWe present a unifying framework for a wide class of iterative methods in numerical linear al...
Several iterative methods are available for solving the ill-posed problem of image reconstruction. T...
Abstract. This paper is concerned with the computation of accurate approximate solutions of linear s...
Abstract. In many inverse problems it is essential to use regularization methods that preserve edges...
The Tikhonov-Phillips method is widely used for regularizing ill-posed problems due to the simplicit...
We consider the reconstruction of images by minimizing regularized cost-functions. To accelerate the...
This paper studies the regularization of constrained Maximum Likelihood iterative algorithms applied...
. Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditio...
Regularization methods are a key tool in the solution of inverse problems. They are used to introduc...
Many computer vision problems are formulated as an objective function consisting of a sum of functio...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...