Add to ORKGIn this thesis a method for the partially norm constrained least squares problem is presented. The method relies on a large-scale trust-region solver and has a low storage requirement. A combination of image misalignment and the inverse problem deblurring illustrates the use of the method
Sparse representation and low-rank approximation are fundamental tools in fields of signal processin...
Image restoration problems are often solved by finding the minimizer of a suitable objective functio...
The problem of finding sparse solutions to underdetermined systems of linear equations is very commo...
In this thesis a method for the partially norm constrained least squares problem is presented. The m...
The need to solve discrete ill-posed problems arises in many areas of science and engineering. Solut...
Straightforward solution of discrete ill-posed least-squares problems with error-contaminated data d...
Many computer vision problems are formulated as an objective function consisting of a sum of functio...
An iterative method to compute the least-squares solutions of the matrix AXB=C over the norm inequal...
AbstractStraightforward solution of discrete ill-posed least-squares problems with error-contaminate...
The problem min ||x||, s.t. ||b-Ax||≤ ε arises in the regularization of discrete forms of ill-posed ...
We consider regularized least-squares problems of the form min {equation presented}. Recently, Zheng...
We consider the solution of large-scale least squares problems where the coefficient matrix comes fr...
Given a linear system Ax ≈ b over the real or complex field where both A and b are subject to noise,...
AbstractLinear least squares problems with box constraints are commonly solved to find model paramet...
Many advances in modern science and technology have resulted in linear ill-posed problems, whose ope...
Sparse representation and low-rank approximation are fundamental tools in fields of signal processin...
Image restoration problems are often solved by finding the minimizer of a suitable objective functio...
The problem of finding sparse solutions to underdetermined systems of linear equations is very commo...
In this thesis a method for the partially norm constrained least squares problem is presented. The m...
The need to solve discrete ill-posed problems arises in many areas of science and engineering. Solut...
Straightforward solution of discrete ill-posed least-squares problems with error-contaminated data d...
Many computer vision problems are formulated as an objective function consisting of a sum of functio...
An iterative method to compute the least-squares solutions of the matrix AXB=C over the norm inequal...
AbstractStraightforward solution of discrete ill-posed least-squares problems with error-contaminate...
The problem min ||x||, s.t. ||b-Ax||≤ ε arises in the regularization of discrete forms of ill-posed ...
We consider regularized least-squares problems of the form min {equation presented}. Recently, Zheng...
We consider the solution of large-scale least squares problems where the coefficient matrix comes fr...
Given a linear system Ax ≈ b over the real or complex field where both A and b are subject to noise,...
AbstractLinear least squares problems with box constraints are commonly solved to find model paramet...
Many advances in modern science and technology have resulted in linear ill-posed problems, whose ope...
Sparse representation and low-rank approximation are fundamental tools in fields of signal processin...
Image restoration problems are often solved by finding the minimizer of a suitable objective functio...
The problem of finding sparse solutions to underdetermined systems of linear equations is very commo...