Regularisation allows one to handle ill-posed inverse problems. Here we focus on discrete unfolding problems. The properties of the results are characterised by the consistency between measurements and unfolding result and by the posterior response matrix. We introduce a novel regularisation scheme based on a discrete-valued penalty function and compare its performance to that of a simple cutoff-regularisation. The discrete-valued penalty function does not require a regularisation parameter that needs to be adjusted on a case-by-case basis. In toy studies very satisfactory results are obtained.Comment: 19 pages, 11 figure
International audienceDue to the ill-posedness of inverse problems, it is important to make use of m...
Abstract. We address discrete nonlinear inverse problems with weighted least squares and Tikhonov re...
In the deterministic context Bakushinskiĭ's theorem excludes the existence of purely data-driven con...
In this paper we consider discrete inverse problems for which noise becomes negligible compared to d...
This study examines, in the framework of variational regularization methods, a multi-penalty regular...
In the deterministic context Bakushinski{\u\i}'s theorem excludes the existence of purely data drive...
In this article we tackle the problem of inverse non linear ill-posed problems from a statistical po...
Discrete ill-posed inverse problems arise in various areas of science and engineering. The presence ...
Inverse problems arise in many applications in science and engineering. They are characterized by th...
A number of regularization methods for discrete inverse problems consist in considering weighted ver...
Many real-world applications are addressed through a linear least-squares problem formulation, whose...
Unfolding is an ill-posed inverse problem in particle physics aiming to infer a true particle-level ...
The regularization of ill-posed systems of equations is carried out by corrections of the data or th...
We address discrete nonlinear inverse problems with weighted least squares and Tikhonov regularizati...
We deal with the solution of a generic linear inverse problem in the Hilbert space setting. The exac...
International audienceDue to the ill-posedness of inverse problems, it is important to make use of m...
Abstract. We address discrete nonlinear inverse problems with weighted least squares and Tikhonov re...
In the deterministic context Bakushinskiĭ's theorem excludes the existence of purely data-driven con...
In this paper we consider discrete inverse problems for which noise becomes negligible compared to d...
This study examines, in the framework of variational regularization methods, a multi-penalty regular...
In the deterministic context Bakushinski{\u\i}'s theorem excludes the existence of purely data drive...
In this article we tackle the problem of inverse non linear ill-posed problems from a statistical po...
Discrete ill-posed inverse problems arise in various areas of science and engineering. The presence ...
Inverse problems arise in many applications in science and engineering. They are characterized by th...
A number of regularization methods for discrete inverse problems consist in considering weighted ver...
Many real-world applications are addressed through a linear least-squares problem formulation, whose...
Unfolding is an ill-posed inverse problem in particle physics aiming to infer a true particle-level ...
The regularization of ill-posed systems of equations is carried out by corrections of the data or th...
We address discrete nonlinear inverse problems with weighted least squares and Tikhonov regularizati...
We deal with the solution of a generic linear inverse problem in the Hilbert space setting. The exac...
International audienceDue to the ill-posedness of inverse problems, it is important to make use of m...
Abstract. We address discrete nonlinear inverse problems with weighted least squares and Tikhonov re...
In the deterministic context Bakushinskiĭ's theorem excludes the existence of purely data-driven con...