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Title:Techniques for Approximating Optimal Linear Estimators of Multidimensional Data
Author(s):Atkinson, Ian Charles
Doctoral Committee Chair(s):Kamalabadi, Farzad
Department / Program:Electrical and Computer Engineering
Discipline:Electrical and Computer Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Engineering, Electronics and Electrical
Abstract:This framework is used to create estimators for four distinct applications. First, we create a blind estimator for hyperspectral and multispectral data that improves the average channel signal-to-noise ratio of a 0 dB observation by 16 dB. Second, we consider the problem of estimating a time-series of optical coherence tomography images and propose a blind estimator that improves visual image quality by reducing the speckle noise that is characteristic of coherent imaging. Next, a blind estimator for fMRI data is constructed that significantly improves the ability to detect low CNR functional activation in small regions of activation without a compromise to the false detection rate. Finally, the concepts developed for the multidimensional estimation framework are used to illustrate how regularized reconstruction of noisy projection data can be improved by exploiting the angular correlation of the true data. In the setting of a filtered back-projection (FBP) reconstruction scheme, this corresponds to performing the filtering step of the well known FBP method in a non-Radon domain. Doing so greatly improves the reconstruction quality of highly noisy projection data.
Issue Date:2007
Description:184 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.
Other Identifier(s):(MiAaPQ)AAI3290459
Date Available in IDEALS:2015-09-25
Date Deposited:2007

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