Regularized Blind Deconvolution with Poisson Data

3rd International Workshop on New Computational Methods for Inverse Problems

物理学;计算机科学

Lecharlier, Loïc^1 ; De Mol, Christine^2

Unité SIMa, Université de Liège Gembloux Agro-Bio Tech, Passage des Déportés 2, 5030 Gembloux, Belgium^1

Department of Mathematics and ECARES, Université Libre de Bruxelles, Campus Plaine CPI 217, Boulevard du Triomphe, 1050 Brussels, Belgium^2

关键词: Blind deconvolution;    Kullback-Leibler;    Multiplicative updates;    Non negatives;    Poisson data;    Stationary points;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/464/1/012003/pdf
DOI  :  10.1088/1742-6596/464/1/012003

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

We propose easy-to-implement algorithms to perform blind deconvolution of nonnegative images in the presence of noise of Poisson type. Alternate minimization of a regularized Kullback-Leibler cost function is achieved via multiplicative update rules. The scheme allows to prove convergence of the iterates to a stationary point of the cost function. Numerical examples are reported to demonstrate the feasibility of the proposed method.