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Proceedings Paper

Bayesian image deblurring and boundary effects
Author(s): Daniela Calvetti; Erkki Somersalo
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Paper Abstract

We consider the deconvolution problem of estimating an image from a noisy blurred version of it. In particular, we are interested in the boundary effects: since the convolution operator is non-local, the blurred image depend on the scenery outside the field of view. Ignoring this dependency leads to image distortion known as boundary effect. In this article, we consider two different approaches to treat the non-locality. One is to estimate the image extended outside the field of view. The other is to treat the influence of the out of view scenery as boundary clutter. both approaches are considered from the Bayesian point of view.

Paper Details

Date Published: 16 September 2005
PDF: 9 pages
Proc. SPIE 5910, Advanced Signal Processing Algorithms, Architectures, and Implementations XV, 59100X (16 September 2005); doi: 10.1117/12.623160
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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