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Proceedings Paper

Local regularization and Bayesian hypermodels
Author(s): Daniela Calvetti; Erkki Somersalo
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Paper Abstract

In this paper, we restore a one-dimensional signal that a priori is known to be a smooth function with a few jump discontinuities from a blurred, noisy specimen signal using a local regularization scheme derived in a Bayesian statistical inversion framework. The proposed method is computationally effective and reproduces well the jump discontinuities, thus is an alternative to using total variation (TV) penalty as a regularizing functional. Our approach avoids the non-differentiability problems encountered in TV methods and is completely data driven in the sense that the parameter selection is done automatically and requires no user intervention. A computed example illustrating the performance of the method when applied to the solution of a deconvolution problem is presented.

Paper Details

Date Published: 16 September 2005
PDF: 9 pages
Proc. SPIE 5910, Advanced Signal Processing Algorithms, Architectures, and Implementations XV, 59100W (16 September 2005); doi: 10.1117/12.623159
Show Author Affiliations
Daniela Calvetti, Case Western Reserve Univ. (United States)
Erkki Somersalo, Helsinki Univ. of Technology (Finland)


Published in SPIE Proceedings Vol. 5910:
Advanced Signal Processing Algorithms, Architectures, and Implementations XV
Franklin T. Luk, Editor(s)

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