Recovering resolution and reducing noise in basis images via optimization methods using physical models

Stephen J. Garnier; Griff L. Bilbro; Wesley E. Snyder

doi:10.1117/12.179228

30 June 1994 Recovering resolution and reducing noise in basis images via optimization methods using physical models

Stephen J. Garnier, Griff L. Bilbro, Wesley E. Snyder

Proceedings Volume 2304, Neural and Stochastic Methods in Image and Signal Processing III; (1994) https://doi.org/10.1117/12.179228
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

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.

Citation Download Citation

Stephen J. Garnier, Griff L. Bilbro, and Wesley E. Snyder "Recovering resolution and reducing noise in basis images via optimization methods using physical models", Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); https://doi.org/10.1117/12.179228

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available