Structured matrices play an important role in the numerical solution of practical problems, because it is possible to develop fast algorithms for their triangular factorization. In this paper we consider a classical problem of Computer Aided Geometric Des

High-resolution image reconstruction refers to reconstructing a higher resolution image from multiple low-resolution samples of a true image. In Chan et al. (Wavelet algorithms for high-resolution image reconstruction, Research Report #CUHK-2000-20, Department of Mathematics, The Chinese University of Hong Kong, 2000), we considered the case where there are no displacement errors in the low-resolution samples, i.e., the samples are aligned properly, and hence the blurring operator is spatially invariant. In this paper, we consider the case where there are displacement errors in the low-resolution samples. The resulting blurring operator is spatially varying and is formed by sampling and summing different spatially invariant blurring operators. We represent each of these spatially invariant blurring operators by a tensor product of a lowpass filter which associates the corresponding blurring operator with a multiresolution analysis of image. Using these filters and their duals, we derive an iterative algorithm to solve the problem based on the algorithmic framework of Chanet al. (Wavelet algorithms for high-resolution image reconstruction, Research Report #CUHK-2000-20, Department of Mathematics, The Chinese University of Hong Kong, 2000). Our algorithm requires a nontrivial modification to the algorithms in Chan et al. (Wavelet algorithms for high-resolution image reconstruction, Research Report #CUHK-2000-20, Department of Mathematics, The Chinese University of Hong Kong, 2000), which apply only to spatially invariant blurring operators. Our numerical examples show that our algorithm gives higher peak signal-to-noise ratios and lower relative errors than those from the Tikhonov least squares approach.