ESTATICO , Claudio

Preconditioning techniques for linear systems are widely used in order to speed up the convergence of iterative methods. Unfortunately, linear systems arising in image processing are highly ill-conditioned and preconditioners often give bad results, since the noise components on the data are strongly amplified already at the early iterations....

In this paper we deal with regularization procedures for the nonlinear inverse problem of atmospheric profile retrievals from measurements of electromagnetic radiations. Since the inverse problem is severely ill-posed, Newton's linearization gives unsatisfactory results. In that case the linearized system is ill-conditioned, and its resolutions...

Popular preconditioners for conjugate gradient methods often reveal poor regularization properties that make them useless for very ill-conditioned linear systems arising in inverse problems. Recent results have awakened the interest towards the Tyrtyshnikov superoptimal preconditioners since it has been demonstrated that they exhibit good...

In this paper we study a fast deconvolution technique for the image restoration problem of the Large Binocular Telescope (LBT) interferometer. Since LBT provides several blurred and noisy images of the same object, it requires the use of multiple-image deconvolution methods in order to produce a unique image with high resolution. Hence the...

No description

Preconditioning techniques for linear systems are widely used in order to speed up the convergence of iterative methods. If the linear system is generated by the discretization of an ill-posed problem, preconditioning may lead to wrong results, since components related to noise on input data are amplified. Using basic concepts from the theory...

No description