Regularized fast multiple-image deconvolution for LBT

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:198

Article; Proceedings Paper

Estatico, Claudio

关键词: multiple-image deconvolution;    ill-posed problems;    iterative regularization;    preconditioning;   

DOI  :  10.1016/j.cam.2005.06.052

来源: Elsevier

【 摘 要 】

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 restoration process is basically a linear ill-posed problem, with overdetermined system and data corrupted by several components of noise. Here the preconditioned conjugate gradient method is used to obtain regularized reconstructions within few iterations. In particular, we study the effectiveness of some preconditioners which have been previously proposed for discrete ill-posed problems. These preconditioners can be considered as regularizing tools since they are able to increase the speed of convergence without amplifying the reconstruction from components with high noise. A wide set of numerical tests will confirm the useful properties of the technique. (c) 2005 Elsevier B.V. All rights reserved.

【 授权许可】

Free