16 November 2000 Multigrid Bayesian methods for optical diffusion tomography
Author Affiliations +
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.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rick P. Millane, Rick P. Millane, Jong Chul Ye, Jong Chul Ye, Charles A. Bouman, Charles A. Bouman, Kevin J. Webb, Kevin J. Webb, } "Multigrid Bayesian methods for optical diffusion tomography", Proc. SPIE 4123, Image Reconstruction from Incomplete Data, (16 November 2000); doi: 10.1117/12.409282; https://doi.org/10.1117/12.409282

Back to Top