Multiple Image Composition and Deblurring ith Spatially Variant PSFs
R. Vio, J. Nagy, L. Tenorio
and W. Wamsteker
In this paper we generalize a reliable and efficient algorithm,
developed in the context of least-square (LS) methods, to estimate the
image corresponding to a given object when a set of observed images
are available with different and spatially invariant PSFs, to deal
with the case of spatially variant PSFs. Noise is assumed additive and
Gaussian. The proposed algorithm allows the use of the classical
single-image deblurring techniq ues for the simultaneous deblurring of
the observed images, with obvious advantages both in computat ional
cost and ease of implementation. Its performance and limitations 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.