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.