Multigrid Bayesian methods for optical diffusion tomography

Details Multigrid Bayesian methods for optical diffusion tomography Multigrid Bayesian methods for optical diffusion tomography

Nov 2000

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2000spie.4123..295m&link_type=abstract

Proc. SPIE Vol. 4123, p. 295-306, Image Reconstruction from Incomplete Data, Michael A. Fiddy; Rick P. Millane; Eds.

Statistics

Computation

2

Scientific paper

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.

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