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Proceedings Paper

Smooth or abrupt: a comparison of regularization methods
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Paper Abstract

In this paper we compare a new regularizing scheme based on the exponential filter function with two classical regularizing methods: Tikhonov regularization and a variant of truncated singular value regularization. The filter functions for the former methods are smooth, but for the latter discontinuous. These regularization methods are applied to the restoration of images degraded by blur and noise. The norm of the noise is assumed to be known, and this allows application of the Morozov discrepancy principle to determine the amount of regularization. We compare the restored images produced by the three regularization methods with optimal values of the regularization parameter. This comparison sheds light on how these different approaches are related.

Paper Details

Date Published: 2 October 1998
PDF: 10 pages
Proc. SPIE 3461, Advanced Signal Processing Algorithms, Architectures, and Implementations VIII, (2 October 1998); doi: 10.1117/12.325689
Show Author Affiliations
Daniela Calvetti, Case Western Reserve Univ. (United States)
Bryan Lewis, Kent State Univ. (United States)
Lothar Reichel, Kent State Univ. (United States)


Published in SPIE Proceedings Vol. 3461:
Advanced Signal Processing Algorithms, Architectures, and Implementations VIII
Franklin T. Luk, Editor(s)

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