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Designing concentration factors to detect jump discontinuities from non-uniform Fourier data


Abstract Edge detection plays a significant role in signal processing and image reconstruction applications where it is used to identify important features in the underlying signal or image. In some of these applications, such as magnetic resonance imaging (MRI), data are sampled in the Fourier domain. When the data are sampled uniformly, a variety of algorithms can be used to efficiently extract the edges of the underlying images. However, in cases where the data are sampled non-uniformly, such as in non-Cartesian MRI, standard inverse Fourier transformation techniques are no longer suitable. Methods exist for handling these types of sampling patterns, but are often ill-equipped for cases where data are highly non-uniform. This thesis further devel... (more)
Created Date 2015-05
Contributor Moore, Rachael (Author) / Gelb, Anne (Thesis Director) / Davis, Jacueline (Committee Member) / Barrett, The Honors College
Subject Applied Mathematics / Signal Processing / Edge Detection / Nonuniform Fourier Data / Fourier Frames
Series Academic Year 2014-2015
Type Text
Extent 23 pages
Language English
Copyright
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Note A copy of this thesis/creative project may be available at Barrett, the Honors College at Arizona State University. If you would like to access the printed copy, please email thesis@asu.edu.
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS


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