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.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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