A&A 434, 795-800 (2005)
DOI: 10.1051/0004-6361:20035754
Multiple-image deblurring with spatially-variant point spread functions
R. Vio1, 2, J. Nagy3 and W. Wamsteker41 Chip Computers Consulting s.r.l., Viale Don L. Sturzo 82, S.Liberale di Marcon, 30020 Venice, Italy
e-mail: robertovio@tin.it
2 ESA-VILSPA, Apartado 50727, 28080 Madrid, Spain
3 Department of Mathematics and Computer Science, Emory University, Atlanta, GA 30322, USA
e-mail: nagy@mathcs.emory.edu
4 ESA-VILSPA, Apartado 50727, 28080 Madrid, Spain
e-mail: willem.wamsteker@esa.int
(Received 27 November 2003 / Accepted 3 January 2005)
Abstract
We generalize a reliable and efficient algorithm, to deal with
the case of spatially-variant PSFs. The algorithm was developed in the
context of a least-square (LS) approach, to estimate the image corresponding
to a given object when a set of observed images are available with
different and spatially-invariant PSFs. Noise is assumed additive and
Gaussian. The proposed algorithm allows the use of the classical LS single-image deblurring techniques
for the simultaneous deblurring of the observed images, with obvious advantages both for computational
cost and ease of implementation. Its performance and limitations, also in the case of Poissonian noise, are
analyzed through numerical simulations. In an appendix we also present
a novel, computationally efficient deblurring algorithm
that is based on a Singular Value Decomposition (SVD) approximation of the variant PSF,
and which is usable with any standard space-invariant direct deblurring algorithm.
The corresponding Matlab code is made available.
Key words: methods: data analysis -- methods: statistical -- techniques: image processing
© ESO 2005

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