{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/24111"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/24111","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Motion compensation from limited data for reference-constrained image reconstruction","abstract":"When reconstructing images from limited (or sparsely sampled) data, reference (or template) images are useful for constraining image reconstruction for various applications. However, in order to be an effective constraint, the reference should be correctly aligned with the target image one wants to reconstruct. Conventional image registration methods assume that both the reference and target images are completely specified, but one usually has only limited data from the target. Therefore, these methods do not apply. This thesis addresses this new problem of registering a known high-resolution reference image to an unknown target image for which one has only limited measurements. We solve this problem by introducing an intermediate image model that expresses the target image as a combination of a generalized series model and a residual component. This model allows the reference and target images to have different contrast and can be used with various motion models. It also makes use of all the available data to estimate the motion parameters. We propose practical algorithms to solve the optimization problems associated with motion parameter estimation. We also analyze the characteristics and performance of the proposed method by an estimation-theoretic approach and by computer simulations. We demonstrate accurate motion parameter estimation for an affine transformation model and a nonrigid deformable model.","abstract_html":"When reconstructing images from limited (or sparsely sampled) data, reference (or template) images are useful for constraining image reconstruction for various applications. However, in order to be an effective constraint, the reference should be correctly aligned with the target image one wants to reconstruct. Conventional image registration methods assume that both the reference and target images are completely specified, but one usually has only limited data from the target. Therefore, these methods do not apply. This thesis addresses this new problem of registering a known high-resolution reference image to an unknown target image for which one has only limited measurements. We solve this problem by introducing an intermediate image model that expresses the target image as a combination of a generalized series model and a residual component. This model allows the reference and target images to have different contrast and can be used with various motion models. It also makes use of all the available data to estimate the motion parameters. We propose practical algorithms to solve the optimization problems associated with motion parameter estimation. We also analyze the characteristics and performance of the proposed method by an estimation-theoretic approach and by computer simulations. We demonstrate accurate motion parameter estimation for an affine transformation model and a nonrigid deformable model.","abstract_has_math":false,"creators":["Lam, Fan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Liang, Zhi-Pei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-25T15:00:13Z","date_published":"2011-05-25T15:00:13Z","updated_at":"2026-07-22T22:25:23Z","subjects":["Reference-constrained image reconstruction","Motion compensation","Generalized series model","Sparse image","Compressed Sensing","Variable projection","Affine transformation","Free-form deformation","Cramer-Rao bound"],"languages":["en"],"rights":["Copyright 2011 Fan Lam"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/24111","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liang, Zhi-Pei"]},{"key":"dc:creator","label":"Author","values":["Lam, Fan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-25T15:00:13Z","2011-05"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Reference-constrained image reconstruction","Motion compensation","Generalized series model","Sparse image","Compressed Sensing","Variable projection","Affine transformation","Free-form deformation","Cramer-Rao bound"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2011 Fan Lam"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/24111"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["When reconstructing images from limited (or sparsely sampled) data, reference (or template) images are useful for constraining image reconstruction for various applications. However, in order to be an effective constraint, the reference should be correctly aligned with the target image one wants to reconstruct. Conventional image registration methods assume that both the reference and target images are completely specified, but one usually has only limited data from the target. Therefore, these methods do not apply. This thesis addresses this new problem of registering a known high-resolution reference image to an unknown target image for which one has only limited measurements. We solve this problem by introducing an intermediate image model that expresses the target image as a combination of a generalized series model and a residual component. This model allows the reference and target images to have different contrast and can be used with various motion models. It also makes use of all the available data to estimate the motion parameters. We propose practical algorithms to solve the optimization problems associated with motion parameter estimation. We also analyze the characteristics and performance of the proposed method by an estimation-theoretic approach and by computer simulations. We demonstrate accurate motion parameter estimation for an affine transformation model and a nonrigid deformable model.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-04-28T13:33:13Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Lam_Fan.rar: 13388929 bytes, checksum: 06c283a9603c5cfa8e4fae3b7a150e66 (MD5) Lam_Fan.pdf: 1885672 bytes, checksum: d679926e3e5d674952999cf3fef63657 (MD5)","Made available in DSpace on 2011-05-25T15:00:13Z (GMT). No. of bitstreams: 3 Lam_Fan.pdf: 1885672 bytes, checksum: d679926e3e5d674952999cf3fef63657 (MD5) Lam_Fan.rar: 13388929 bytes, checksum: 06c283a9603c5cfa8e4fae3b7a150e66 (MD5) license.txt: 4056 bytes, checksum: 404bdc111efa62bae853bc57e11afef9 (MD5)"]},{"key":"dc:title","label":"Title","values":["Motion compensation from limited data for reference-constrained image reconstruction"]}]}],"canonical_facts":{"dc:contributor":["Liang, Zhi-Pei"],"dc:creator":["Lam, Fan"],"dc:date":["2011-05-25T15:00:13Z","2011-05"],"dc:description":["When reconstructing images from limited (or sparsely sampled) data, reference (or template) images are useful for constraining image reconstruction for various applications. However, in order to be an effective constraint, the reference should be correctly aligned with the target image one wants to reconstruct. Conventional image registration methods assume that both the reference and target images are completely specified, but one usually has only limited data from the target. Therefore, these methods do not apply. This thesis addresses this new problem of registering a known high-resolution reference image to an unknown target image for which one has only limited measurements. We solve this problem by introducing an intermediate image model that expresses the target image as a combination of a generalized series model and a residual component. This model allows the reference and target images to have different contrast and can be used with various motion models. It also makes use of all the available data to estimate the motion parameters. We propose practical algorithms to solve the optimization problems associated with motion parameter estimation. We also analyze the characteristics and performance of the proposed method by an estimation-theoretic approach and by computer simulations. We demonstrate accurate motion parameter estimation for an affine transformation model and a nonrigid deformable model.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-04-28T13:33:13Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Lam_Fan.rar: 13388929 bytes, checksum: 06c283a9603c5cfa8e4fae3b7a150e66 (MD5) Lam_Fan.pdf: 1885672 bytes, checksum: d679926e3e5d674952999cf3fef63657 (MD5)","Made available in DSpace on 2011-05-25T15:00:13Z (GMT). No. of bitstreams: 3 Lam_Fan.pdf: 1885672 bytes, checksum: d679926e3e5d674952999cf3fef63657 (MD5) Lam_Fan.rar: 13388929 bytes, checksum: 06c283a9603c5cfa8e4fae3b7a150e66 (MD5) license.txt: 4056 bytes, checksum: 404bdc111efa62bae853bc57e11afef9 (MD5)"],"dc:identifier":["http://hdl.handle.net/2142/24111"],"dc:language":["en"],"dc:rights":["Copyright 2011 Fan Lam"],"dc:subject":["Reference-constrained image reconstruction","Motion compensation","Generalized series model","Sparse image","Compressed Sensing","Variable projection","Affine transformation","Free-form deformation","Cramer-Rao bound"],"dc:title":["Motion compensation from limited data for reference-constrained image reconstruction"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:23Z"}