Techniques for Approximating Optimal Linear Estimators of Multidimensional Data

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Atkinson, Ian Charles

Contributors dc:contributor

• Kamalabadi, Farzad

• Engineering, Electronics and Electrical

Language dc:language

eng

Identifier

(MiAaPQ)AAI3290459

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81052