Abstract
In this paper we present a fully data-driven and locally-adaptive method for image reconstruction that is based on the concept of statistical multiresolution estimation as introduced in [1]. It constitutes a statistical regularization technique that uses a ℓ ∞ -type distance measure as data fidelity combined with a convex cost functional. The resulting convex optimization problem is approached by a combination of an inexact augmented Lagrangian method and Dykstra’s projection algorithm.
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Frick, K., Marnitz, P. (2012). A Statistical Multiresolution Strategy for Image Reconstruction. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2011. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24785-9_7
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DOI: https://doi.org/10.1007/978-3-642-24785-9_7
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