{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/98062"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/98062","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Deep learning-based optimization of light field processing","abstract":"As commonly acknowledged, light field technology offers a multi-dimensional extension for image processing by capturing both the intensity and direction of light rays, resulting in a more comprehensive representation of the real world and significantly enhancing human perception. However, the increased light rays’ capacity and density present challenges in information storage and transmission. Moreover, traditional display technology is limited in its ability to depict directional and depth information, and current light field display technology is inferior to high-definition displays. Therefore, in this the- sis, we propose a light-field-specific task-driven downsampling pre-processing algorithm and a depth-calibration deep learning framework designed for light field technology to ad- dress these issues. In experiment, we validate the effectiveness of our proposed methods from both quantitative and qualitative perspectives. These light field technology enhance- ment methods can be incorporated into the framework for light field processing.","abstract_html":"As commonly acknowledged, light field technology offers a multi-dimensional extension for image processing by capturing both the intensity and direction of light rays, resulting in a more comprehensive representation of the real world and significantly enhancing human perception. However, the increased light rays’ capacity and density present challenges in information storage and transmission. Moreover, traditional display technology is limited in its ability to depict directional and depth information, and current light field display technology is inferior to high-definition displays. Therefore, in this the- sis, we propose a light-field-specific task-driven downsampling pre-processing algorithm and a depth-calibration deep learning framework designed for light field technology to ad- dress these issues. In experiment, we validate the effectiveness of our proposed methods from both quantitative and qualitative perspectives. These light field technology enhance- ment methods can be incorporated into the framework for light field processing.","abstract_has_math":false,"creators":["Sun, Yangfan, 1989-"],"institution":"University of Missouri--Kansas City","degree_name":"Ph.D. (Doctor of Philosophy)","degree_level":"Doctoral","degree_discipline":"Computer Networking and Communication Systems (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Li, Zhu"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T05:18:08Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/98062","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Li, Zhu"]},{"key":"dc:creator","label":"Author","values":["Sun, Yangfan, 1989-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-01-30T17:05:10Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-01-30T17:05:10Z"]},{"key":"dc:date.issued","label":"Date","values":["2023"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Networking and Communication Systems (UMKC)","Electrical and Computer Engineering (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D. (Doctor of Philosophy)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Kansas City"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/98062"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page, viewed January 31, 2024","Dissertation advisor: Zhu Li","Vita","Includes bibliographical references (pages 83-94)","Dissertation (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2023"]},{"key":"dc:description.abstract","label":"Abstract","values":["As commonly acknowledged, light field technology offers a multi-dimensional extension for image processing by capturing both the intensity and direction of light rays, resulting in a more comprehensive representation of the real world and significantly enhancing human perception. However, the increased light rays’ capacity and density present challenges in information storage and transmission. Moreover, traditional display technology is limited in its ability to depict directional and depth information, and current light field display technology is inferior to high-definition displays. Therefore, in this the- sis, we propose a light-field-specific task-driven downsampling pre-processing algorithm and a depth-calibration deep learning framework designed for light field technology to ad- dress these issues. In experiment, we validate the effectiveness of our proposed methods from both quantitative and qualitative perspectives. These light field technology enhance- ment methods can be incorporated into the framework for light field processing."]},{"key":"dc:title","label":"Title","values":["Deep learning-based optimization of light field processing"]}]}],"canonical_facts":{"dc:contributor.advisor":["Li, Zhu"],"dc:creator":["Sun, Yangfan, 1989-"],"dc:date.accessioned":["2024-01-30T17:05:10Z"],"dc:date.available":["2024-01-30T17:05:10Z"],"dc:date.issued":["2023"],"dc:description":["Title from PDF of title page, viewed January 31, 2024","Dissertation advisor: Zhu Li","Vita","Includes bibliographical references (pages 83-94)","Dissertation (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2023"],"dc:description.abstract":["As commonly acknowledged, light field technology offers a multi-dimensional extension for image processing by capturing both the intensity and direction of light rays, resulting in a more comprehensive representation of the real world and significantly enhancing human perception. However, the increased light rays’ capacity and density present challenges in information storage and transmission. Moreover, traditional display technology is limited in its ability to depict directional and depth information, and current light field display technology is inferior to high-definition displays. Therefore, in this the- sis, we propose a light-field-specific task-driven downsampling pre-processing algorithm and a depth-calibration deep learning framework designed for light field technology to ad- dress these issues. In experiment, we validate the effectiveness of our proposed methods from both quantitative and qualitative perspectives. These light field technology enhance- ment methods can be incorporated into the framework for light field processing."],"dc:identifier.uri":["https://hdl.handle.net/10355/98062"],"dc:title":["Deep learning-based optimization of light field processing"],"thesis:degree_discipline":["Computer Networking and Communication Systems (UMKC)","Electrical and Computer Engineering (UMKC)"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph.D. (Doctor of Philosophy)"],"thesis:institution_name":["University of Missouri--Kansas City"]},"updated_at":"2026-07-24T05:18:08Z"}