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Proceedings Paper

Deconvolution in a ridgelet and curvelet domain
Author(s): Glenn R. Easley; Carlos A. Berenstein; Dennis M. Healy
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Paper Abstract

We present techniques for performing image reconstruction based on deconvolution in the Radon domain. To deal with a variety of possible boundary conditions, we work with a corresponding generalized discrete Radon transform in order to obtain projection slices for deconvolution. By estimating the projections using wavelet techniques, we are able to do deconvolution directly in a ridgelet domain. We also show how this method can be carried out locally, so that deconvolution can be done in a curvelet domain as well. These techniques suggest a whole new paradigm for developing deconvolution algorithms, which can incorporate leading deconvolution schemes. We conclude by showing experimental results indicating that these new algorithms can significantly improve upon current leading deconvolution methods.

Paper Details

Date Published: 28 March 2005
PDF: 11 pages
Proc. SPIE 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, (28 March 2005); doi: 10.1117/12.602822
Show Author Affiliations
Glenn R. Easley, System Planning Corp. (United States)
Carlos A. Berenstein, Univ. of Maryland/College Park (United States)
Dennis M. Healy, Univ. of Maryland/College Park (United States)


Published in SPIE Proceedings Vol. 5818:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III
Harold H. Szu, Editor(s)

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