Multichannel Methods for Restoration in Computed Imaging
Morrison, Robert Lee, Jr
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https://hdl.handle.net/2142/81016
Description
Title
Multichannel Methods for Restoration in Computed Imaging
Author(s)
Morrison, Robert Lee, Jr
Issue Date
2007
Doctoral Committee Chair(s)
Do, Minh N.
Department of Study
Electrical and Computer Engineering
Discipline
Electrical and Computer Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
This dissertation addresses data-driven image restoration for computed imaging systems. The work is focused on problems in two imaging modalities: the autofocus problem in synthetic aperture radar (SAR), and the problem of estimating coil sensitivities in parallel magnetic resonance imaging (PMRI). A common thread in both problems is their inherent multichannel nature, i.e., both exhibit special structure due to the redundancy provided by multiple signal measurements. By explicitly exploiting the multichannel structure, novel algorithms are developed offering improved restoration performance. We first present a theoretical study providing more insight into metric-based SAR autofocus techniques. Our analytical results show how metric-based methods implicitly rely on the multichannel defocusing model of SAR autofocus to form well-focused restorations. Utilizing the multichannel structure of the SAR autofocus problem explicitly, we develop a new noniterative restoration approach termed the MultiChannel Autofocus (MCA) algorithm. In this approach, the focused image is directly recovered using a linear algebraic formulation. Experimental results using actual and simulated SAR data demonstrate that MCA provides superior performance in comparison with existing autofocus methods. Lastly, we develop a new subspace-based approach for estimating receiver coil sensitivity functions used in PMRI reconstruction. Our approach does not rely on sum-of-squares assumptions used in previous PMRI techniques, thus avoiding potential problems such as poor image contrast and aliasing artifacts.
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