Abstract
The structured total least squares (STLS) problem has been introduced to handle problems involving structured matrices corrupted by noise. Often the problem is ill-posed. Recently, regularization has been proposed in the STLS framework to solve ill-posed blind deconvolution problems encountered in image deblurring when both the image and the blurring function have uncertainty. The kernel of the regularized STLS (RSTLS) problem is a least squares problem involving Block–Toeplitz–Toeplitz–Block matrices. In this paper an algorithm is described to solve this problem, based on a particular implementation of the generalized Schur Algorithm (GSA). It is shown that this new implementation improves the computational efficiency of the straightforward implementation of GSA from O( N 2.5) to O( N 2), where N is the number of pixels in the image.
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