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Proceedings Paper

Recovering resolution and reducing noise in basis images via optimization methods using physical models
Author(s): Stephen J. Garnier; Griff L. Bilbro; Wesley E. Snyder
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Paper Abstract

This work addresses an optimization approach to sensor fusion and applies the technique to magnetic resonance image (MRI) restoration. Several images are related using a physical model (spin equation) to corresponding basis images. The basis images (proton density and two nuclear relaxation times) are determined from the MRI data and subsequently used to obtain excellent restorations. The method also has been applied to image restoration problems in other domains. All images are modeled as Markov random fields (MRF). Four maximum a posteriori (MAP) restorations are presented. The `product' and `sum' forms for basis (signal) and spatial correlations are discussed, compared, and evaluated for various situations and features. A novel method of global optimization necessary for the nonlinear techniques is also introduced. This approach to sensor fusion, using global optimization, MRF models, and Bayesian techniques, has been generalized and applied to other problem domains, such as the restoration of multiple-modality laser range and luminance signals.

Paper Details

Date Published: 30 June 1994
PDF: 11 pages
Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); doi: 10.1117/12.179228
Show Author Affiliations
Stephen J. Garnier, North Carolina State Univ. (United States)
Griff L. Bilbro, North Carolina State Univ. (United States)
Wesley E. Snyder, North Carolina State Univ. (United States)


Published in SPIE Proceedings Vol. 2304:
Neural and Stochastic Methods in Image and Signal Processing III
Su-Shing Chen, Editor(s)

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