Regularized Blind Deconvolution with Poisson Data

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 ite...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2013-01, Vol.464 (1), p.12003-5
Main Authors: Lecharlier, Loïc, De Mol, Christine
Format: Article
Language:English
Subjects:
Algorithms
Blinds
Convergence
Cost function
Deconvolution
Feasibility
Mathematical models
Optimization
Physics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/464/1/012003