{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81016"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81016","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Multichannel Methods for Restoration in Computed Imaging","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Morrison, Robert Lee, Jr"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Do, Minh N."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:09:14Z","date_published":"2015-09-25T20:09:14Z","updated_at":"2026-07-22T22:26:15Z","subjects":["Engineering, Electronics and Electrical"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3269986"],"render_values":[{"text":"(MiAaPQ)AAI3269986","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81016","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Do, Minh N."]},{"key":"dc:creator","label":"Author","values":["Morrison, Robert Lee, Jr"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:09:14Z","10000-01-01","2007"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering, Electronics and Electrical"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/81016","(MiAaPQ)AAI3269986"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Made available in DSpace on 2015-09-25T20:09:14Z (GMT). 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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.","Made available in DSpace on 2015-09-25T20:09:14Z (GMT). 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