Numerical Algorithms

, Volume 60, Issue 1, pp 169–188

An improved Newton projection method for nonnegative deblurring of Poisson-corrupted images with Tikhonov regularization

Original Paper

DOI: 10.1007/s11075-011-9517-y

Cite this article as:
Landi, G. & Loli Piccolomini, E. Numer Algor (2012) 60: 169. doi:10.1007/s11075-011-9517-y

Abstract

In this paper a quasi-Newton projection method for image deblurring is presented. The image restoration problem is mathematically formulated as a nonnegatively constrained minimization problem where the objective function is the sum of the Kullback–Leibler divergence, used to express fidelity to the data in the presence of Poisson noise, and of a Tikhonov regularization term. The Hessian of the objective function is approximated so that the Newton system can be efficiently solved by using Fast Fourier Transforms. The numerical results show the potential of the proposed method both in terms of relative error reduction and computational efficiency.

Keywords

Nonnegatively constrained minimization Regularization Image deblurring Newton projection method Poisson noise 

Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of BolognaBolognaItaly

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