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Proceedings Paper

Regularization Theory In Discrete Image Restoration
Author(s): Nicolaos B. Karayiannis; Anastasios N. Venetsanopoulos
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Paper Abstract

This paper presents several aspects of the application of Regularization Theory in image restoration. This is accomplished by extending the applicability of the stabilizing functional approach to 2-D ill-posed inverse problems. Image restoration is formulated as the constrained minimization of a stabilizing functional. The analytical study of this optimization problem results in a variety of regularized solutions. A relationship between these regularized solutions and optimal Wiener estimation is identified. The resulting algorithms are evaluated through experimental results.

Paper Details

Date Published: 25 October 1988
PDF: 12 pages
Proc. SPIE 1001, Visual Communications and Image Processing '88: Third in a Series, (25 October 1988); doi: 10.1117/12.968935
Show Author Affiliations
Nicolaos B. Karayiannis, University of Toronto (Canada)
Anastasios N. Venetsanopoulos, University of Toronto (Canada)


Published in SPIE Proceedings Vol. 1001:
Visual Communications and Image Processing '88: Third in a Series
T. Russell Hsing, Editor(s)

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