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Proceedings Paper

Multigrid Bayesian methods for optical diffusion tomography
Author(s): Rick P. Millane; Jong Chul Ye; Charles A. Bouman; Kevin J. Webb
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Paper Abstract

Optical diffusion imaging is a new imaging modality that promises great potential in applications such as medical imaging, environmental sensing and nondestructive testing. It presents a difficult nonlinear image reconstruction problem however. An inversion algorithm is formulated in Bayesian framework, and an efficient optimization technique that uses iterative coordinate descent is presented. A general multigrid optimization technique for nonlinear image reconstruction problems is developed and applied to the optical diffusion imaging problem. Numerical results show that this approach improves the quality of reconstructions and dramatically decreases computation times.

Paper Details

Date Published: 16 November 2000
PDF: 12 pages
Proc. SPIE 4123, Image Reconstruction from Incomplete Data, (16 November 2000); doi: 10.1117/12.409282
Show Author Affiliations
Rick P. Millane, Purdue Univ. (New Zealand)
Jong Chul Ye, Univ. of Illinois/Urbana-Champaign (United States)
Charles A. Bouman, Purdue Univ. (United States)
Kevin J. Webb, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 4123:
Image Reconstruction from Incomplete Data
Michael A. Fiddy; Rick P. Millane, Editor(s)

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