An alternating minimization method for blind deconvolution from Poisson data

4th International Workshop on New Computational Methods for Inverse Problems

物理学;计算机科学

Prato, Marco^1 ; La Camera, Andrea^2 ; Bonettini, Silvia^3

Dipartimento di Scienze Fisiche, Informatiche e Matematiche, Università di Modena e Reggio Emilia, Via Campi 213/b, Modena

41125, Italy^1

Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria Dei Sistemi, Università di Genova, Via Dodecaneso 35, Genova

16145, Italy^2

Dipartimento di Matematica e Informatica, Università di Ferrara, Via Saragat 1, Ferrara

44122, Italy^3

关键词: Acquisition systems;    Adaptive optics systems;    Alternating minimization;    Astronomical images;    Blind deconvolution;    Constrained minimization problem;    Kullback Leibler divergence;    Optimization tools;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/542/1/012006/pdf
DOI  :  10.1088/1742-6596/542/1/012006

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Blind deconvolution is a particularly challenging inverse problem since information on both the desired target and the acquisition system have to be inferred from the measured data. When the collected data are affected by Poisson noise, this problem is typically addressed by the minimization of the Kullback-Leibler divergence, in which the unknowns are sought in particular feasible sets depending on the a priori information provided by the specific application. If these sets are separated, then the resulting constrained minimization problem can be addressed with an inexact alternating strategy. In this paper we apply this optimization tool to the problem of reconstructing astronomical images from adaptive optics systems, and we show that the proposed approach succeeds in providing very good results in the blind deconvolution of nondense stellar clusters.